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An Online Adjustment Based Node Placement Mechanism for the NFV-enabled MEC Network

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Abstract

Multi-access Edge Computing (MEC) aims to reduce mobile services latency and free users from resource constraints by deploying cloud services closer to users. However, with the change of network condition, the service requirements of users cannot be fulfilled due to the fixed deployment of MEC nodes. In this case, the placement of MEC nodes attracts more and more researchers’ attentions. Particularly, in the circumstance with Network Function Virtualization (NFV), MEC functions are allowed to be deployed on any edge node that has the NFV Infrastructure (NFVI), and these MEC-function-enabled edge nodes can become MEC nodes. In this case, how to deploy these MEC nodes flexibly to cope with the dynamic changes of network load becomes very important. In this paper, we propose an Online Adjustment based MEC node Placement mechanism (OAMP). First, the node placement problem is constructed as a class of set coverage problem based on the average historical load of nodes. The backtracking algorithm of depth-first search is used to obtain the optimal initial placement strategy. Then, based on users’ QoE (quality of experience), the fuzzy neural network is used to determine whether the deployment of MEC nodes needs to be adjusted. Finally, the number and location of MEC nodes are updated intelligently by Deep Q-Network (DQN) algorithm. The proposed OAMP aims to solve where to deploy MEC nodes, and how to adjust the deployment in response to dynamic changes in the network. Simulation results show that OAMP can effectively reduce the deployment cost while ensuring users’ QoE, and achieve lower Service Level Agreement (SLA) violation rate.

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References

  1. Mach P, Becvar Z (2017) Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656. thirdquarter

    Article  Google Scholar 

  2. Sonkoly B, Czentye J, Szalay M, Németh B, Toka L (2021) Survey on placement methods in the edge and beyond. IEEE Commun Surv Tutor 23(4):2590–2629

    Article  Google Scholar 

  3. Yang S, Li F, Shen M, Chen X, Fu X, Wang Y (2019) Cloudlet placement and task allocation in mobile edge computing. IEEE Internet Things J 6(3):5853–5863

    Article  Google Scholar 

  4. Jia M, Cao J, Liang W (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput 5(4):725–737

    Article  Google Scholar 

  5. Zhao L, Sun W, Shi Y, Liu J (2018) Optimal placement of cloudlets for access delay minimization in sdn-based internet of things networks. IEEE Internet Things J 5(2):1334–1344

    Article  Google Scholar 

  6. Tan H, Han Z, Li X, Lau F. C. M. (2017) Online job dispatching and scheduling in edge-clouds. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp 1–9

  7. Gao H, Zhang Y, Miao H, Barroso RJD, Yang X (2021) Sdtioa: Modeling the timed privacy requirements of iot service composition: A user interaction perspective for automatic transformation from bpel to timed automata. Mobile Networks and Applications

  8. Ma X, Xu H, Gao H, Bian M (2021) Real-time multiple-workflow scheduling in cloud environments. IEEE Trans Netw Serv Manag 18(4):4002–4018

    Article  Google Scholar 

  9. ETSI (2020) Mobile-edge computing (mec) framework and reference architecture, version 2.2.1

  10. Huang Y, Xu H, Gao H, Ma X, Hussain W (2021) Ssur: an approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center. IEEE Trans Green Commun Netw 5(2):670–681

    Article  Google Scholar 

  11. Fan Q, Ansari N (2018) Cost aware cloudlet placement for big data processing at the edge. In: 2017 IEEE International Conference on Communications (ICC)

  12. Yang L, Cao J, Liang G, Han X (2016) Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput 65(5):1440–1452

    Article  MathSciNet  Google Scholar 

  13. Wang G., Xu F., Zhao C. (2020) Multi-access edge computing based vehicular network: Joint task scheduling and resource allocation strategy. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6

  14. Xu X, Shen B, Yin X, Khosravi MR, Wu H, Qi L, Wan S (2021) Edge server quantification and placement for offloading social media services in industrial cognitive iov. IEEE Trans Industr Inform 17(4):2910–2918

    Article  Google Scholar 

  15. Kasi SK, Kasi MK, Ali K, Raza M, Afzal H, Lasebae A, Naeem B, Islam SU, Rodrigues JJPC (2021) Heuristic edge server placement in industrial internet of things and cellular networks. IEEE Internet Things J 8(13):10308–10317

    Article  Google Scholar 

  16. Cao B, Fan S, Zhao J, Tian S, Zheng Z, Yan Y, Yang P (2021) Large-scale many-objective deployment optimization of edge servers. IEEE Trans Intell Transp Syst 22(6):3841–3849

    Article  Google Scholar 

  17. Yan Z., Cola T.D., Zhao K., Li W., Du S., Yang H. (2021) Exploiting edge computing in internet of space things networks: Dynamic and static server placement. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pp. 1–6

  18. Yang L, Yao H, Wang J, Jiang C, Benslimane A, Liu Y (2020) Multi-uav-enabled load-balance mobile-edge computing for iot networks. IEEE Internet Things J 7(8):6898–6908

    Article  Google Scholar 

  19. Toosi AN, Son J, Chi Q, Buyya R (2019) Elasticsfc: Auto-scaling techniques for elastic service function chaining in network functions virtualization-based clouds. J Syst Softw 152(JUN):108–119

    Article  Google Scholar 

  20. Yala L, Frangoudis PA, Ksentini A (2018) Latency and availability driven vnf placement in a mec-nfv environment, in. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp 1–7

  21. Sarrigiannis I, Ramantas K, Kartsakli E, Mekikis P, Antonopoulos A, Verikoukis C (2020) Online vnf lifecycle management in a mec-enabled 5g iot architecture. IEEE Internet Things J 7(5):4183–4194

    Article  Google Scholar 

  22. Chantre HD, da Fonseca NLS (2018) Multi-objective optimization for edge device placement and reliable broadcasting in 5g nfv-based small cell networks. IEEE J Sel Areas Commun 36(10):2304–2317

    Article  Google Scholar 

  23. Tseng H-W, Yang T-T, Hsu F-T (2021) An mec-based vnf placement and scheduling scheme for ar application topology, in. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp 1–6

  24. Yuan X, Sun M, Lou W (2020) A dynamic deep-learning-based virtual edge node placement scheme for edge cloud systems in mobile environment. IEEE Trans Cloud Comput, 1–1

  25. Zhu Y, Zhang W, Chen Y, Gao H (2019) A novel approach to workload prediction using attention-based lstm encoder-decoder network in cloud environment. EURASIP Journal on Wireless Communications and Networking, 2019(1)

  26. Liu Y, Pei J, Hong P, Li D (2019) Cost-efficient virtual network function placement and traffic steering. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pp 1–6

  27. Yang B, Chai WK, Xu Z, Katsaros KV, Pavlou G (2018) Cost-efficient nfv-enabled mobile edge-cloud for low latency mobile applications. IEEE Trans Netw Serv Manag 15(1):475–488

    Article  Google Scholar 

  28. Gao H, Qin X, Barroso RJD, Hussain W, Xu Y, Yin Y (2020) Collaborative learning-based industrial iot api recommendation for software-defined devices: The implicit knowledge discovery perspective. IEEE Transactions on Emerging Topics in Computational Intelligence, 1–11

  29. Dieye M, Ahvar S, Sahoo J, Ahvar E, Glitho R, Elbiaze H, Crespi N (2018) Cpvnf: Cost-efficient proactive vnf placement and chaining for value-added services in content delivery networks. IEEE Trans Netw Serv Manag 15(2):774–786

    Article  Google Scholar 

  30. Current JR, Storbeck JE (1988) Capacitated covering models. Environ Plan B Plan Design 15(2):153–163

    Article  Google Scholar 

  31. Chang L, Qin F, Li A (2015) A novel backtracking scheme for attitude determination-based initial alignment. IEEE Trans Autom Sci Eng 12(1):384–390

    Article  Google Scholar 

  32. Barbehenn M (1998) A note on the complexity of dijkstra’s algorithm for graphs with weighted vertices. IEEE Trans Comput 47(2):263–274

    Article  MathSciNet  Google Scholar 

  33. Jang JR (1993) Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber 23(3):665–685

    Article  Google Scholar 

  34. Yin Y, Huang Q, Gao H, Xu Y (2021) Personalized apis recommendation with cognitive knowledge mining for industrial systems. IEEE Trans Ind Inform 17(9):6153–6161

    Article  Google Scholar 

  35. He X, Wang K, Huang H, Miyazaki T, Wang Y, Guo S (2018) Green resource allocation based on deep reinforcement learning in content-centric iot. IEEE Transactions on Emerging Topics in Computing, 1–1

  36. 3GPP (2020) Service requirements for the 5g system, ts 22.261, v18.0.0 release 18

  37. Knight S, Nguyen HX, Falkner N, Bowden R, Roughan M (2011) The internet topology zoo. IEEE J Select Areas Commun 29(9):1765–1775

    Article  Google Scholar 

  38. CPLEX II (2009) V12. 1: User manual for cplex. Int. Bus. Mach. Corporat. 46(53):108–157

    Google Scholar 

  39. Li J, Gao H, Lv T, Lu Y (2018) Deep reinforcement learning based computation offloading and resource allocation for mec. In: 2018 IEEE wireless communications and networking conference (WCNC), pp 1–6

  40. Xu Y, Wu Y, Gao H, Song S, Yin Y, Xiao X (2021) Collaborative apis recommendation for artificial intelligence of things with information fusion. Futur Gener Comput Syst 125:471–479

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62071075 and 61971077, in part by the General Project of Natural Science Foundation of Chongqing under Grant cstc2020jcyjmsxmX0704 and cstc2019jcyj-msxmX0575, and in part by the Fundamental Research Funds for Central Universities under Grant 2020CDJ-LHZZ-022.

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Authors and Affiliations

  1. School of Microelectronics and Communication Enginneering, Chongqing University, Chongqing, 400044, China

    Liang Liang, Jinguo Qin, Peng Jiang, Zhengchuan Chen & Yunjian Jia

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  1. Liang Liang
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  2. Jinguo Qin
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  3. Peng Jiang
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  4. Zhengchuan Chen
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  5. Yunjian Jia
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Correspondence to Liang Liang.

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Liang, L., Qin, J., Jiang, P. et al. An Online Adjustment Based Node Placement Mechanism for the NFV-enabled MEC Network. Mobile Netw Appl 27, 1490–1505 (2022). https://doi.org/10.1007/s11036-022-01976-w

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