Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
116 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
3 tokens/sec
DeepSeek R1 via Azure Pro
55 tokens/sec
2000 character limit reached

Availability-aware Service Placement Policy in Fog Computing Based on Graph Partitions (2401.12690v1)

Published 23 Jan 2024 in cs.NI

Abstract: This paper presents a policy for service placement of fog applications inspired on complex networks and graph theory. We propose a twofold partition process based on communities for the partition of the fog devices and based on transitive closures for the application services partition. The allocation of the services is performed sequentially by, firstly, mapping applications to device communities and, secondly, mapping service transitive closures to fog devices in the community. The underlying idea is to place as many inter-related services as possible in the most nearby devices to the users. The optimization objectives are the availability of the applications and the Quality of Service (QoS) of the system, measured as the number of requests that are executed before the application deadlines. We compared our solution with an Integer Linear Programming approach, and the simulation results showed that our proposal obtains higher QoS and availability when fails in the nodes are considered.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (28)
  1. O. Consortium et al., “Openfog reference architecture for fog computing,” Tech. Rep., February, Tech. Rep., 2017.
  2. S. Filiposka, A. Mishev, and K. Gilly, “Community-based allocation and migration strategies for fog computing,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC), April 2018, pp. 1–6.
  3. Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos, “Fog orchestration for internet of things services,” IEEE Internet Computing, vol. 21, no. 2, pp. 16–24, Mar 2017.
  4. O. Skarlat, M. Nardelli, S. Schulte, M. Borkowski, and P. Leitner, “Optimized iot service placement in the fog,” Service Oriented Computing and Applications, Oct 2017. [Online]. Available: https://doi.org/10.1007/s11761-017-0219-8
  5. A. Brogi and S. Forti, “Qos-aware deployment of iot applications through the fog,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1185–1192, Oct 2017.
  6. C. Guerrero, I. Lera, and C. Juiz, “A lightweight decentralized service placement policy for performance optimization in fog computing,” Journal of Ambient Intelligence and Humanized Computing, Jun 2018. [Online]. Available: https://doi.org/10.1007/s12652-018-0914-0
  7. L. Ni, J. Zhang, C. Jiang, C. Yan, and K. Yu, “Resource allocation strategy in fog computing based on priced timed petri nets,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1216–1228, Oct 2017.
  8. R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, and K. K. Leung, “Dynamic service migration and workload scheduling in edge-clouds,” Performance Evaluation, vol. 91, no. Supplement C, pp. 205 – 228, 2015, special Issue: Performance 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0166531615000619
  9. L. Gu, D. Zeng, S. Guo, A. Barnawi, and Y. Xiang, “Cost efficient resource management in fog computing supported medical cyber-physical system,” IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 1, pp. 108–119, Jan 2017.
  10. K. Velasquez, D. P. Abreu, M. Curado, and E. Monteiro, “Service placement for latency reduction in the internet of things,” Annals of Telecommunications, vol. 72, no. 1, pp. 105–115, Feb 2017. [Online]. Available: https://doi.org/10.1007/s12243-016-0524-9
  11. Z. Huang, K.-J. Lin, S.-Y. Yu, and J. Y. jen Hsu, “Co-locating services in iot systems to minimize the communication energy cost,” Journal of Innovation in Digital Ecosystems, vol. 1, no. 1, pp. 47 – 57, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2352664515000061
  12. L. Yang, J. Cao, G. Liang, and X. Han, “Cost aware service placement and load dispatching in mobile cloud systems,” IEEE Transactions on Computers, vol. 65, no. 5, pp. 1440–1452, May 2016.
  13. V. B. C. Souza, W. Ramírez, X. Masip-Bruin, E. Marín-Tordera, G. Ren, and G. Tashakor, “Handling service allocation in combined fog-cloud scenarios,” in 2016 IEEE International Conference on Communications (ICC), May 2016, pp. 1–5.
  14. D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, “Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system,” IEEE Transactions on Computers, vol. 65, no. 12, pp. 3702–3712, Dec 2016.
  15. S. Filiposka, A. Mishev, and C. Juiz, “Community-based vm placement framework,” The Journal of Supercomputing, vol. 71, no. 12, pp. 4504–4528, Dec 2015. [Online]. Available: https://doi.org/10.1007/s11227-015-1546-1
  16. C. Guerrero, I. Lera, and C. Juiz, “On the influence of fog colonies partitioning in fog application makespan,” in 2019 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), August 2018.
  17. I. Lera, C. Guerrero, and C. Juiz, “Comparing centrality indices for network usage optimization of data placement policies in fog devices,” in 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), April 2018, pp. 115–122.
  18. Y. Elkhatib, B. Porter, H. B. Ribeiro, M. F. Zhani, J. Qadir, and E. Rivière, “On using micro-clouds to deliver the fog,” IEEE Internet Computing, vol. 21, no. 2, pp. 8–15, Mar 2017.
  19. A. Yousefpour, G. Ishigaki, R. Gour, and J. P. Jue, “On reducing iot service delay via fog offloading,” IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–1, 2018.
  20. M. Vogler, J. M. Schleicher, C. Inzinger, and S. Dustdar, “A scalable framework for provisioning large-scale iot deployments,” ACM Trans. Internet Technol., vol. 16, no. 2, pp. 11:1–11:20, Mar. 2016. [Online]. Available: http://doi.acm.org/10.1145/2850416
  21. A. Krylovskiy, M. Jahn, and E. Patti, “Designing a smart city internet of things platform with microservice architecture,” in 2015 3rd International Conference on Future Internet of Things and Cloud, Aug 2015, pp. 25–30.
  22. E. Saurez, K. Hong, D. Lillethun, U. Ramachandran, and B. Ottenwälder, “Incremental deployment and migration of geo-distributed situation awareness applications in the fog,” in Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, ser. DEBS ’16.   New York, NY, USA: ACM, 2016, pp. 258–269. [Online]. Available: http://doi.acm.org/10.1145/2933267.2933317
  23. A. Balalaie, A. Heydarnoori, and P. Jamshidi, “Microservices architecture enables devops: Migration to a cloud-native architecture,” IEEE Software, vol. 33, no. 3, pp. 42–52, May 2016.
  24. M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Phys. Rev. E, vol. 69, no. 2, p. 026113, Feb. 2004. [Online]. Available: http://link.aps.org/doi/10.1103/PhysRevE.69.026113
  25. S. Fortunato, V. Latora, and M. Marchiori, “Method to find community structures based on information centrality,” Phys. Rev. E, vol. 70, p. 056104, Nov 2004. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevE.70.056104
  26. A. Alahmadi, A. Alnowiser, M. M. Zhu, D. Che, and P. Ghodous, “Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud,” in 2014 International Conference on Computational Science and Computational Intelligence, vol. 2, March 2014, pp. 69–74.
  27. H. S. Warren Jr, “A modification of warshall’s algorithm for the transitive closure of binary relations,” Communications of the ACM, vol. 18, no. 4, pp. 218–220, 1975.
  28. I. Lera and C. Guerrero, “Yafs, yet another fog simulator,” https://github.com/acsicuib/YAFS, accessed: 2018-02-03.
Citations (89)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com