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Distributed Asynchronous Service Deployment in the Edge-Cloud Multi-tier Network (2312.11187v2)

Published 18 Dec 2023 in cs.NI

Abstract: In an edge-cloud multi-tier network, datacenters provide services to mobile users, with each service having specific latency constraints and computational requirements. Deploying such a variety of services while matching their requirements with the available computing resources is challenging. In addition, time-critical services may have to be migrated as the users move, to keep fulfilling their latency constraints. Unlike previous work relying on an orchestrator with an always-updated global view of the available resources and the users' locations, this work envisions a distributed solution to the above problems. In particular, we propose a distributed asynchronous framework for service deployment in the edge-cloud that increases the system resilience by avoiding a single point of failure, as in the case of a central orchestrator. Our solution ensures cost-efficient feasible placement of services, while using negligible bandwidth. Our results, obtained through trace-driven, large-scale simulations, show that the proposed solution provides performance very close to those obtained by state-of-the-art centralized solutions, and at the cost of a small communication overhead.

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References (51)
  1. I. Cohen, P. Giaccone, and C. F. Chiasserini, “Distributed asynchronous protocol for service provisioning in the edge-cloud continuum,” in 2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM).   IEEE, 2023, pp. 1–6.
  2. T. Taleb, A. Ksentini, and P. A. Frangoudis, “Follow-me cloud: When cloud services follow mobile users,” IEEE Transactions on Cloud Computing, vol. 7, no. 2, pp. 369–382, 2016.
  3. A. Ullah, H. Dagdeviren, R. C. Ariyattu, J. DesLauriers, T. Kiss, and J. Bowden, “Micado-edge: Towards an application-level orchestrator for the cloud-to-edge computing continuum,” Journal of Grid Computing, vol. 19, no. 4, pp. 1–28, 2021.
  4. D. Zhao, G. Sun, D. Liao, S. Xu, and V. Chang, “Mobile-aware service function chain migration in cloud–fog computing,” Future Generation Computer Systems, vol. 96, pp. 591–604, 2019.
  5. S. Svorobej, M. Bendechache, F. Griesinger, and J. Domaschka, “Orchestration from the cloud to the edge,” The Cloud-to-Thing Continuum, pp. 61–77, 2020.
  6. R. Bruschi, F. Davoli, P. Lago, and J. F. Pajo, “Move with me: Scalably keeping virtual objects close to users on the move,” in IEEE ICC, 2018, pp. 1–6.
  7. Y.-D. Lin, C.-C. Wang, C.-Y. Huang, and Y.-C. Lai, “Hierarchical cord for NFV datacenters: resource allocation with cost-latency tradeoff,” IEEE Network, vol. 32, no. 5, pp. 124–130, 2018.
  8. L. Tong, Y. Li, and W. Gao, “A hierarchical edge cloud architecture for mobile computing,” in IEEE INFOCOM, 2016, pp. 1–9.
  9. G. Sun, D. Liao, D. Zhao, Z. Xu, and H. Yu, “Live migration for multiple correlated virtual machines in cloud-based data centers,” IEEE Transactions on Services Computing, pp. 279–291, 2015.
  10. B. Kar, K.-M. Shieh, Y.-C. Lai, Y.-D. Lin, and H.-W. Ferng, “QoS violation probability minimization in federating vehicular-fogs with cloud and edge systems,” IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13 270–13 280, 2021.
  11. I. Cohen, C. F. Chiasserini, P. Giaccone, and G. Scalosub, “Dynamic service provisioning in the edge-cloud continuum with bounded resources,” IEEE Transaction on Networking, 2023.
  12. M. Dieye, S. Ahvar, J. Sahoo, E. Ahvar, R. Glitho, H. Elbiaze, and N. Crespi, “CPVNF: Cost-efficient proactive VNF placement and chaining for value-added services in content delivery networks,” IEEE Transaction on Network and Service Management, vol. 15, no. 2, pp. 774–786, 2018.
  13. H. Yu, J. Yang, and C. Fung, “Elastic network service chain with fine-grained vertical scaling,” in IEEE GLOBECOM, 2018, pp. 1–7.
  14. A. Al-Dulaimy, J. Taheri, A. Kassler, M. R. HoseinyFarahabady, S. Deng, and A. Zomaya, “MULTISCALER: A multi-loop auto-scaling approach for cloud-based applications,” IEEE Transactions on Cloud Computing, 2020.
  15. I. Leyva-Pupo, C. Cervelló-Pastor, C. Anagnostopoulos, and D. P. Pezaros, “Dynamic scheduling and optimal reconfiguration of UPF placement in 5G networks,” in ACM MSWiM, 2020, pp. 103–111.
  16. S. Agarwal, F. Malandrino, C. F. Chiasserini, and S. De, “Vnf placement and resource allocation for the support of vertical services in 5g networks,” IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 433–446, 2019.
  17. X. Sun and N. Ansari, “PRIMAL: Profit maximization avatar placement for mobile edge computing,” in IEEE ICC, 2016, pp. 1–6.
  18. T. Mahboob, Y. R. Jung, and M. Y. Chung, “Dynamic VNF placement to manage user traffic flow in software-defined wireless networks,” Journal of Network and Systems Management, Springer, pp. 1–21, 2020.
  19. I. Cohen, G. Einziger, M. Goldstein, Y. Sa’ar, G. Scalosub, and E. Waisbard, “High throughput vms placement with constrained communication overhead and provable guarantees,” IEEE Transactions on Network and Service Management, 2023.
  20. V. Mancuso, P. Castagno, M. Sereno, and M. A. Marsan, “Stateful versus stateless selection of edge or cloud servers under latency constraints,” in 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).   IEEE, 2022, pp. 110–119.
  21. A. De La Oliva et al., “Final 5g-crosshaul system design and economic analysis,” 5G-Crosshaul public deliverable, 2017.
  22. T. Ouyang et al., “Adaptive user-managed service placement for mobile edge computing: An online learning approach,” in IEEE INFOCOM, 2019, pp. 1468–1476.
  23. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic service migration in mobile edge-clouds,” in IEEE IFIP Networking, 2015, pp. 1–9.
  24. J. Martín-Pérez, F. Malandrino, C. F. Chiasserini, M. Groshev, and C. J. Bernardos, “Multiagent graph coloring: Pareto efficiency, fairness and individual rationality,” in KPI Guarantees in Network Slicing, vol. 30, no. 2, 2022, pp. 655–668.
  25. M. Nguyen, M. Dolati, and M. Ghaderi, “Deadline-aware SFC orchestration under demand uncertainty,” IEEE Transactions on Network and Service Management, pp. 2275–2290, 2020.
  26. L. Codecá, R. Frank, S. Faye, and T. Engel, “Luxembourg SUMO traffic (LuST) scenario: Traffic demand evaluation,” IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 2, pp. 52–63, 2017.
  27. L. Codeca and J. Härri, “Monaco SUMO traffic (MoST) scenario: A 3D mobility scenario for cooperative ITS,” EPiC Series in Engineering, vol. 2, pp. 43–55, 2018.
  28. “Opencellid,” https://opencellid.org/, accessed on 3.10.2021.
  29. “Gurobi optimizer reference manual,” 2023. [Online]. Available: https://www.gurobi.com
  30. M. Goudarzi, M. Palaniswami, and R. Buyya, “A distributed application placement and migration management techniques for edge and fog computing environments,” in IEEE FedCSIS, 2021, pp. 37–56.
  31. T. Gao et al., “Cost-efficient VNF placement and scheduling in public cloud networks,” IEEE Transactions on Communications, pp. 4946–4959, 2020.
  32. P. Alvarez et al., “Microscopic traffic simulation using sumo,” in IEEE International Conference on Intelligent Transportation Systems, 2018.
  33. “Service function chains migration.” [Online]. Available: https://github.com/ofanan/SFC_migration
  34. “OMNeT++ discrete event simulator,” 2023. [Online]. Available: https://omnetpp.org
  35. “Distributed SFC migration.” [Online]. Available: https://github.com/ofanan/Distributed_SFC_migration
  36. H. Hawilo, M. Jammal, and A. Shami, “Orchestrating network function virtualization platform: Migration or re-instantiation?” in IEEE CloudNet, 2017, pp. 1–6.
  37. C. Puliafito, E. Mingozzi, C. Vallati, F. Longo, and G. Merlino, “Companion fog computing: Supporting things mobility through container migration at the edge,” in IEEE SMARTCOMP, 2018, pp. 97–105.
  38. M. Ghaznavi, N. Shahriar, S. Kamali, R. Ahmed, and R. Boutaba, “Distributed service function chaining,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2479–2489, 2017.
  39. D. Haja, M. Szabo, M. Szalay, A. Nagy, A. Kern, L. Toka, and B. Sonkoly, “How to orchestrate a distributed openstack,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).   IEEE, 2018, pp. 293–298.
  40. V. Mancuso, L. Badia, P. Castagno, M. Sereno, and M. A. Marsan, “Efficiency of distributed selection of edge or cloud servers under latency constraints,” in 2023 21st Mediterranean Communication and Computer Networking Conference (MedComNet).   IEEE, 2023, pp. 158–166.
  41. M. S. Hung and J. C. Fisk, “An algorithm for 0-1 multiple-knapsack problems,” Naval Research Logistics Quarterly, vol. 25, no. 3, pp. 571–579, 1978.
  42. K. Ha et al., “You can teach elephants to dance: Agile VM handoff for edge computing,” in ACM/IEEE SEC, 2017, pp. 1–14.
  43. R. Stoyanov and M. J. Kollingbaum, “Efficient live migration of linux containers,” in ISC High Performance.   Springer, 2018, pp. 184–193.
  44. A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live service migration in mobile edge clouds,” IEEE Wireless Communications, pp. 140–147, 2017.
  45. K. A. Noghani, A. Kassler, and P. S. Gopannan, “EVPN/SDN assisted live VM migration between geo-distributed data centers,” in IEEE NetSoft, 2018, pp. 105–113.
  46. R. Cziva, C. Anagnostopoulos, and D. P. Pezaros, “Dynamic, latency-optimal VNF placement at the network edge,” in IEEE INFOCOM, 2018, pp. 693–701.
  47. S. Ramanathan, K. Kondepu, M. Razo, M. Tacca, L. Valcarenghi, and A. Fumagalli, “Live migration of virtual machine and container based mobile core network components: A comprehensive study,” IEEE Access, vol. 9, pp. 105 082–105 100, 2021.
  48. T. He, A. N. Toosi, and R. Buyya, “SLA-aware multiple migration planning and scheduling in SDN-NFV-enabled clouds,” Journal of Systems and Software, vol. 176, p. 110943, 2021.
  49. T. Subramanya and R. Riggio, “Centralized and federated learning for predictive VNF autoscaling in multi-domain 5G networks and beyond,” IEEE TNSM, vol. 18, no. 1, pp. 63–78, 2021.
  50. V. Eramo et al., “Reconfiguration of optical-nfv network architectures based on cloud resource allocation and qos degradation cost-aware prediction techniques,” IEEE Access, vol. 8, pp. 200 834–200 850, 2020.
  51. V. Eramo and T. Catena, “Application of an innovative convolutional/LSTM neural network for computing resource allocation in nfv network architectures,” IEEE TNSM, 2022.

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