2000 character limit reached
WIDESim: A toolkit for simulating resource management techniques of scientific Workflows In Distributed Environments with graph topology (2206.03538v3)
Published 7 Jun 2022 in cs.DC and cs.NI
Abstract: IoT devices trigger real-time applications by receiving data from their vicinity. Modeling these applications in the form of workflows enables automating their procedure, especially for the business and industry. Depending on the features of the applications, they can be modeled in different forms, including single workflow, multiple workflows, and workflow ensembles. Since the whole data must be sent to the cloud servers for processing and storage, cloud computing has many challenges for executing real-time applications, such as bandwidth limitation, delay, and privacy. Edge paradigms are introduced to address the challenges of cloud computing in executing IoT applications. Executing IoT applications using device-to-device communications in edge paradigms requiring direct communication between devices in a network with a graph topology. While there is no simulator supporting simulating workflow-based applications and device-to-device communication, this paper introduces a toolkit for simulating resource management of scientific workflows in distributed environments with graph topology called WIDESim.The graph topology of WIDESim enables D2D communications in edge paradigms. WIDESim can work with all three different structures of scientific workflows: single, multiple workflows, and workflow ensembles. It has no constraint on the topology of the distributed environment. Also, unlike most existing network simulators, this simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators and workflow management tools. Also, we have evaluated its performance in different scenarios of distributed computing systems using different types of workflow-based applications. The results indicate that WIDESim's performance is close to existing standard simulators besides its improvements.
- Siar, H., Izadi, M.: Offloading coalition formation for scheduling scientific workflow ensembles in fog environments. Journal of Grid Computing 19(3), 1–20 (2021) Rodriguez and Buyya [2018] Rodriguez, M.A., Buyya, R.: Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Future Generation Computer Systems 79, 739–750 (2018) De Donno et al. [2019] De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Rodriguez, M.A., Buyya, R.: Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Future Generation Computer Systems 79, 739–750 (2018) De Donno et al. [2019] De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Rodriguez, M.A., Buyya, R.: Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Future Generation Computer Systems 79, 739–750 (2018) De Donno et al. [2019] De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. Ieee Access 7, 150936–150948 (2019) Karagiannis et al. [2021] Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Transactions on Cloud Computing (2021) Rabay’a et al. [2019] Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Rabay’a, A., Schleicher, E., Graffi, K.: Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 82–89 (2019). IEEE Yousefpour et al. [2019] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98, 289–330 (2019) Toczé and Nadjm-Tehrani [2018] Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wireless Communications and Mobile Computing 2018 (2018) Buyya and Srirama [2019] Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, ??? (2019) Yu et al. [2015] Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Cooperative resource management in cloud-enabled vehicular networks. IEEE Transactions on Industrial Electronics 62(12), 7938–7951 (2015) Stavrinides and Karatza [2019] Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time iot workflows in fog and cloud environments. Multimedia Tools and Applications 78(17), 24639–24655 (2019) Chen and Xu [2017] Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Chen, L., Xu, J.: Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pp. 1–11 (2017) Lim et al. [2020] Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Lim, W.Y.B., Ng, J.S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Yang, Q.: Incentive mechanism design for resource sharing in collaborative edge learning. arXiv preprint arXiv:2006.00511 (2020) Bianzino et al. [2014] Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Bianzino, A.P., Rougier, J.-L., Chaudet, C., Rossi, D., et al.: The green-game: Accounting for device criticality in resource consolidation for backbone ip networks. Strategic Behavior and the Environment 4(2), 131–153 (2014) Goudarzi et al. [2020] Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing 20(4), 1298–1311 (2020) Zhang et al. [2019] Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Zhang, C., Du, H., Ye, Q., Liu, C., Yuan, H.: Dmra: a decentralized resource allocation scheme for multi-sp mobile edge computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 390–398 (2019). IEEE Tripathi et al. [2017] Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Tripathi, R., Vignesh, S., Tamarapalli, V., Chronopoulos, A.T., Siar, H.: Non-cooperative power and latency aware load balancing in distributed data centers. Journal of Parallel and Distributed Computing 107, 76–86 (2017) Jošilo and Dán [2018] Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Jošilo, S., Dán, G.: Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27(1), 85–97 (2018) Guerrero et al. [2019] Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10(6), 2435–2452 (2019) Gupta et al. [2017] Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(9), 1275–1296 (2017) Chen and Deelman [2012] Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Chen, W., Deelman, E.: Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-science, pp. 1–8 (2012). IEEE Liu et al. [2019] Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Liu, X., Fan, L., Xu, J., Li, X., Gong, L., Grundy, J., Yang, Y.: Fogworkflowsim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1114–1117 (2019). IEEE Calheiros et al. [2011] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011) Hameed et al. [2016] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016) Zhang et al. [2010] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications 1(1), 7–18 (2010) Singh and Chana [2016] Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing 14(2), 217–264 (2016) Yi et al. [2015] Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42 (2015) Zhang et al. [2017] Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine 55(8), 52–57 (2017) Mahmud et al. [2020] Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys (CSUR) 53(4), 1–43 (2020) Xu et al. [2021] Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Xu, H., Qiu, X., Zhang, W., Liu, K., Liu, S., Chen, W.: Privacy-preserving incentive mechanism for multi-leader multi-follower iot-edge computing market: A reinforcement learning approach. Journal of Systems Architecture 114, 101932 (2021) Li et al. [2020] Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Li, Z., Xu, M., Nie, J., Kang, J., Chen, W., Xie, S.: Noma-enabled cooperative computation offloading for blockchain-empowered internet of things: A learning approach. IEEE Internet of Things Journal 8(4), 2364–2378 (2020) Ijaz et al. [2021] Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Ijaz, S., Munir, E.U., Ahmad, S.G., Rafique, M.M., Rana, O.F.: Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9), 2033–2059 (2021) Hong et al. [2019] Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Hong, Z., Chen, W., Huang, H., Guo, S., Zheng, Z.: Multi-hop cooperative computation offloading for industrial iot–edge–cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 30(12), 2759–2774 (2019) Zeng et al. [2016] Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65(12), 3702–3712 (2016) Mahmud et al. [2018] Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology (TOIT) 19(1), 1–21 (2018) Xu et al. [2019] Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for iot-enabled cloud-edge computing. Future Generation Computer Systems 95, 522–533 (2019) de Souza Toniolli and Jaumard [2019] Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Souza Toniolli, J.L., Jaumard, B.: Resource allocation for multiple workflows in cloud-fog computing systems. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 77–84 (2019) De Maio and Kimovski [2020] De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Future Generation Computer Systems 106, 171–184 (2020) Bharathi et al. [2008] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, pp. 1–10 (2008). IEEE Genez et al. [2017] Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Genez, T.A.L., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.M.: A robust scheduler for workflow ensembles under uncertainties of available bandwidth. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 504–511 (2017). IEEE Jiang et al. [2015] Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Jiang, Q., Lee, Y.C., Zomaya, A.Y.: Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529 (2015). IEEE Malawski et al. [2015] Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Future Generation Computer Systems 48, 1–18 (2015) Taylor et al. [2007] Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., et al.: Workflows for e-Science: Scientific Workflows for Grids vol. 1. Springer, ??? (2007) Genez et al. [2016] Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Genez, T.A., Bittencourt, L.F., Sakellariou, R., Madeira, E.R.: A flexible scheduler for workflow ensembles. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, pp. 55–62 (2016) Pietri et al. [2013] Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Sakellariou, R.: Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, pp. 34–41 (2013). IEEE Mahmud et al. [2022] Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Mahmud, R., Pallewatta, S., Goudarzi, M., Buyya, R.: ifogsim2: An extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software 190, 111351 (2022) Jha et al. [2020] Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Jha, D.N., Alwasel, K., Alshoshan, A., Huang, X., Naha, R.K., Battula, S.K., Garg, S., Puthal, D., James, P., Zomaya, A., et al.: Iotsim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Software: Practice and Experience 50(6), 844–867 (2020) Sonmez et al. [2017] Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: An environment for performance evaluation of edge computing systems, 39–44 (2017) Juve et al. [2013] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013) Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)
- Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future generation computer systems 29(3), 682–692 (2013)