Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Resilient Edge Service Placement under Demand and Node Failure Uncertainties (2107.04748v3)

Published 10 Jul 2021 in math.OC and cs.DC

Abstract: Resiliency plays a critical role in designing future communication networks. How to make edge computing systems resilient against unpredictable failures and fluctuating demand is an important and challenging problem. To this end, this paper investigates a resilient service placement and workload allocation problem for a service provider (SP) who can procure resources from numerous edge nodes to serve its users, considering both resource demand and node failure uncertainties. We introduce a novel two-stage adaptive robust model to capture this problem. The service placement and resource procurement decisions are optimized in the first stage while the workload allocation decision is determined in the second stage after the uncertainty realization. By exploiting the special structure of the uncertainty set, we develop an efficient iterative algorithm that can converge to an exact optimal solution within a finite number of iterations. We further present an affine decision rule approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results demonstrate the advantages of the proposed model and approaches, which can help the SP make proactive decisions to mitigate the impacts of the uncertainties.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016.
  2. D. T. Nguyen, H. T. Nguyen, N. Trieu, and V. K. Bhargava, “Two-stage robust edge service placement and sizing under demand uncertainty,” IEEE Internet Things J., vol. 9, no. 2, pp. 1560–1574, 2022.
  3. S. Chaisiri, B.-S. Lee, and D. Niyato, “Optimization of resource provisioning cost in cloud computing,” IEEE Transactions on Services Computing, vol. 5, no. 2, pp. 164–177, 2012.
  4. J. Chase and D. Niyato, “Joint optimization of resource provisioning in cloud computing,” IEEE Transactions on Services Computing, vol. 10, no. 3, pp. 396–409, 2017.
  5. H. Badri, T. Bahreini, D. Grosu, and K. Yang, “Energy-aware application placement in mobile edge computing: A stochastic optimization approach,” IEEE Trans. Parallel Distrib. Syst., vol. 31, no. 4, pp. 909–922, 2019.
  6. D. Bertsimas and M. Sim, “The price of robustness,” J. Oper. Res., vol. 52, no. 1, pp. 35–53, 2004.
  7. D. Chemodanov, P. Calyam, F. Esposito, R. McGarvey, K. Palaniappan, and A. Pescapé, “A near optimal reliable orchestration approach for geo-distributed latency-sensitive sfcs,” IEEE Trans. Netw. Sci. Eng., vol. 7, no. 4, pp. 2730–2745, 2020.
  8. F. He and E. Oki, “Backup allocation model with probabilistic protection for virtual networks against multiple facility node failures,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 3, pp. 2943–2959, 2021.
  9. M. Johnston, H.-W. Lee, and E. Modiano, “A robust optimization approach to backup network design with random failures,” IEEE/ACM Trans. Netw., vol. 23, no. 4, pp. 1216–1228, 2014.
  10. R. Kang, F. He, and E. Oki, “Robust virtual network function allocation in service function chains with uncertain availability schedule,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 3, pp. 2987–3005, 2021.
  11. R. Kaewpuang, D. Niyato, P. Wang, and E. Hossain, “A framework for cooperative resource management in mobile cloud computing,” IEEE J. Sel. Areas Commun., vol. 31, no. 12, pp. 2685–2700, 2013.
  12. Y. Qu, D. Lu, H. Dai, H. Tan, S. Tang, F. Wu, and C. Dong, “Resilient service provisioning for edge computing,” IEEE Internet Things J., 2021.
  13. D. T. A. Nguyen, J. Cheng, N. Trieu, and D. T. Nguyen, “A fairness-aware attacker-defender model for optimal edge network operation and protection,” IEEE Networking Letters, vol. 5, no. 2, pp. 120–124, 2023.
  14. B. Zeng and L. Zhao, “Solving two-stage robust optimization problems using a column-and-constraint generation method,” Oper. Res. L., vol. 41, no. 5, pp. 457–461, 2013.
  15. D. Bertsimas and V. Goyal, “On the power and limitations of affine policies in two-stage adaptive optimization,” Math. Program., vol. 134, no. 2, pp. 491–531, 2012.
  16. “Amazon ec2 instance,” https://aws.amazon.com/ec2/pricing/, Access June 2021.
  17. A. Ben-Tal, A. Goryashko, E. Guslitzer, and A. Nemirovski, “Adjustable robust solutions of uncertain linear programs,” Math. Program., vol. 99, no. 2, pp. 351–376, 2004.
  18. Z. Wang and M. Qi, “Robust service network design under demand uncertainty,” Trans. Sci., vol. 54, no. 3, pp. 676–689, 2020.
  19. A. Ardestani-Jaafari and E. Delage, “Linearized robust counterparts of two-stage robust optimization problems with applications in operations management,” INFORMS J. Comput., vol. 33, no. 3, pp. 1138–1161, 2021.
  20. Y. An, B. Zeng, Y. Zhang, and L. Zhao, “Reliable p-median facility location problem: two-stage robust models and algorithms,” Trans. Res., vol. 64, pp. 54–72, 2014.
  21. J. Cheng, D. T. A. Nguyen, L. Wang, D. T. Nguyen, and V. K. Bhargava, “Resilient edge service placement under demand and node failure uncertainties - technical report,” https://doi.org/10.48550/arXiv.2107.04748, 2023.
  22. J. Fortuny-Amat and B. McCarl, “A representation and economic interpretation of a two-level programming problem,” J. Oper. Res. Soc., vol. 32, no. 9, pp. 783–792, 1981.
  23. D. Kuhn, W. Wiesemann, and A. Georghiou, “Primal and dual linear decision rules in stochastic and robust optimization,” Math. Program., vol. 130, no. 1, pp. 177–209, 2011.
  24. M. Jia, J. Cao, and W. Liang, “Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks,” IEEE Trans. on Cloud Comput., vol. 5, no. 4, pp. 725–737, 2017.
  25. Y. Jia, C. Wu, Z. Li, F. Le, and A. Liu, “Online scaling of nfv service chains across geo-distributed datacenters,” IEEE/ACM Trans. Netw., vol. 26, no. 2, pp. 699–710, 2018.
  26. D. T. Nguyen, L. B. Le, and V. Bhargava, “Price-based resource allocation for edge computing: A market equilibrium approach,” IEEE Trans. Cloud Comput., vol. 9, no. 1, pp. 302–317, 2021.
  27. http://gwa.ewi.tudelft.nl/datasets/, Access Dec 2021.
  28. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surv. Tut., vol. 19, no. 4, pp. 2322–2358, 2017.
  29. D. T. Nguyen, L. B. Le, and V. K. Bhargava, “A market-based framework for multi-resource allocation in fog computing,” IEEE/ACM Trans. Netw., vol. 27, no. 3, pp. 1151–1164, 2019.
  30. V. Farhadi, F. Mehmeti, T. He, T. F. L. Porta, H. Khamfroush, S. Wang, K. S. Chan, and K. Poularakis, “Service placement and request scheduling for data-intensive applications in edge clouds,” IEEE/ACM Trans. Netw., vol. 29, no. 2, pp. 779–792, 2021.
  31. S. Pasteris, S. Wang, M. Herbster, and T. He, “Service placement with provable guarantees in heterogeneous edge computing systems,” in Proc. IEEE INFOCOM, 2019, pp. 514–522.
  32. R. Yu, G. Xue, and X. Zhang, “Provisioning qos-aware and robust applications in internet of things: a network perspective,” IEEE/ACM Trans. Netw., vol. 27, no. 5, pp. 1931–1944, 2019.
  33. D. T. A. Nguyen, J. Cheng, D. T. Nguyen, and A. Nedich, “Crowdcache: A decentralized game-theoretic framework for mobile edge content sharing,” 21th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), Singapore, 2023.
  34. D. T. Nguyen, L. B. Le, and V. Bhargava, “Edge computing resource procurement: An online optimization approach,” in Proc. IEEE WF-IoT, 2018, pp. 807–812.
  35. T. Ouyang, R. Li, X. Chen, Z. Zhou, and X. Tang, “Adaptive user-managed service placement for mobile edge computing: An online learning approach,” in Proc. IEEE INFOCOM, 2019, pp. 1468–1476.
  36. J. Li, W. Liang, and Y. Ma, “Robust service provisioning with service function chain requirements in mobile edge computing,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 2138–2153, 2021.
  37. L. Li, D. Shi, R. Hou, X. Li, J. Wang, H. Li, and M. Pan, “Data-driven optimization for cooperative edge service provisioning with demand uncertainty,” IEEE Internet Things J., vol. 8, no. 6, pp. 4317–4328, 2021.
  38. J. Cheng, D. T. A. Nguyen, L. Wang, D. T. Nguyen, and V. K. Bhargava, “A bandit approach to online pricing for heterogeneous edge resource allocation,” IEEE Conference on Network Softwarization (NetSoft), Spain, 2023.
  39. R. Cziva, C. Anagnostopoulos, and D. P. Pezaros, “Dynamic, latency-optimal vnf placement at the network edge,” in Proc. IEEE INFOCOM, 2018, pp. 693–701.
  40. R. Gouareb, V. Friderikos, and A.-H. Aghvami, “Virtual network functions routing and placement for edge cloud latency minimization,” IEEE J. Sel. Areas Commun., vol. 36, no. 10, pp. 2346–2357, 2018.
  41. P. Zhao and G. Dán, “A benders decomposition approach for resilient placement of virtual process control functions in mobile edge clouds,” IEEE Trans. Netw. Serv. Manag., vol. 15, no. 4, pp. 1460–1472, 2018.
  42. T. Gao, X. Li, Y. Wu, W. Zou, S. Huang, M. Tornatore, and B. Mukherjee, “Cost-efficient vnf placement and scheduling in public cloud networks,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 4946–4959, 2020.
  43. P. Jin, X. Fei, Q. Zhang, F. Liu, and B. Li, “Latency-aware vnf chain deployment with efficient resource reuse at network edge,” in Proc. IEEE INFOCOM, 2020, pp. 267–276.
  44. N. Promwongsa, A. Ebrahimzadeh, R. H. Glitho, and N. Crespi, “Joint vnf placement and scheduling for latency-sensitive services,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 4, pp. 2432–2449, 2022.
  45. A. Aral and I. Brandić, “Learning spatiotemporal failure dependencies for resilient edge computing services,” IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 7, pp. 1578–1590, 2020.
  46. N. Kherraf, S. Sharafeddine, C. M. Assi, and A. Ghrayeb, “Latency and reliability-aware workload assignment in iot networks with mobile edge clouds,” IEEE Trans. Netw. Ser. Manag., vol. 16, no. 4, pp. 1435–1449, 2019.
  47. D. Chemodanov, P. Calyam, F. Esposito, R. McGarvey, K. Palaniappan, and A. Pescapé, “A near-optimal reliable orchestration approach for geo-distributed latency-sensitive sfcs,” IEEE Trans. Netw. Sci. Eng., vol. 7, no. 4, pp. 2730–2745, 2020.
  48. S. Bian, X. Huang, Z. Shao, X. Gao, and Y. Yang, “Service chain composition with resource failures in nfv systems: A game-theoretic perspective,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 1, pp. 224–239, 2020.
  49. P. A. Apostolopoulos, E. E. Tsiropoulou, and S. Papavassiliou, “Risk-aware data offloading in multi-server multi-access edge computing environment,” IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1405–1418, 2020.
Citations (9)
View on Semantic Scholar

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now