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Edge server placement problem in multi-access edge computing environment: models, techniques, and applications

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Abstract

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented.

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References

  1. Goudarzi, M. et al., Scheduling IoT applications in edge and fog computing environments: a taxonomy and future directions, ACM Comput. Surv., 55(7), 1-41 (2022).

    Article  Google Scholar 

  2. IDC.: IoT Growth Demands Rethink of Long-Term Storage Strategies. (2020) https://www.idc.com/getdoc.jsp?containerId=prAP46737220. Accessed 20 Oct 2021

  3. Business Insider.: The Internet of Things 2020. https://www.businessinsider.com/internet-of-things-report. Accessed 20 Oct 2021

  4. Jia, M., et al.: Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans. Cloud Comput. 5(4), 725–737 (2015)

    Article  MathSciNet  Google Scholar 

  5. Yao J. et al.: QoS-aware fog resource provisioning and mobile device power control in IoT networks, In: IEEE Transactions on Network and Service Management, (2018).

  6. Liu, Y., et al.: Towards edge intelligence: multi-access edge computing for 5G and internet of things. IEEE Internet Things J. 7(8), 6722–6747 (2020)

    Article  Google Scholar 

  7. Khan, W.-Z., et al.: Edge computing: a survey. Futur. Gener. Comput. Syst. 97, 219–235 (2019)

    Article  Google Scholar 

  8. Rafique, W., et al.: Complementing IoT services through software defined networking and edge computing: a comprehensive survey. IEEE Commun. Surv. Tutor. 22, 1761–1804 (2020)

    Article  Google Scholar 

  9. Ranaweera, P., et al.: Survey on multi-access edge computing security and privacy. IEEE Commun. Surv. Tutor. 23(2), 1078–1124 (2021)

    Article  Google Scholar 

  10. Rahdari, F., Khayyambashi, M.R., Movahhedinia, N.: A QoE-aware nonlinear fuzzy radio resource management approach for revenue enhancement. IEEE Syst. J. (2022).

  11. Sonkoly, B., et al.: Survey on placement methods in the edge and beyond. IEEE Commun. Surv. Tutor. 3(4), 2590–2629 (2021)

    Article  Google Scholar 

  12. Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials 19(3), 1628–1656 (2017)

    Article  Google Scholar 

  13. Mukherjee, M., et al.: Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun. Surv. Tutor. 20(3), 1826–1857 (2018)

    Article  Google Scholar 

  14. Ren, J., et al.: A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing mobile edge computing, fog computing, and cloudlet. ACM Comput. Surv. 52(6), 1–36 (2019)

    Article  MathSciNet  Google Scholar 

  15. Wang, H., et al.: Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles. IEEE Commun. Surv. Tutor. 22(4), 2349–2377 (2020)

    Article  Google Scholar 

  16. Mao, Y., et al.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv Tutorials 19(4), 2322–2358 (2017)

    Article  Google Scholar 

  17. Feng, C. et al.: Computation offloading in mobile edge computing networks: a survey. J. Netw. Comput. Appl. (2022).

  18. Sabella, D., et al.: Multi-Access Edge Computing in Action. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  19. Hu, Y.-C. et al.: Mobile edge computing: a key technology towards 5G, ETSI White Paper, 11 (2015)

  20. Baktir, A.-C., et al.: How can edge computing benefit from software-defined networking: a survey use cases & future directions. IEEE Commun. Surv. Tutorials 19(4), 2359–2391 (2017)

    Article  Google Scholar 

  21. Shi, W., et al.: Edge computing: Vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  22. Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)

    Article  Google Scholar 

  23. Chang, Z., et al.: A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things. IEEE Internet Things J. 8(18), 13849–13875 (2021)

    Article  Google Scholar 

  24. Khan, L.-U., et al.: Edge-computing-enabled smart cities: a comprehensive survey. IEEE Internet Things J. 7(10), 10200–10232 (2020)

    Article  Google Scholar 

  25. Ahmed, E., Rehmani, M.H.: Mobile edge computing: opportunities, solutions, and challenges. Futur. Gener. Comput. Syst. 70, 59–63 (2017)

    Article  Google Scholar 

  26. Verbelen, T., et al.: Leveraging cloudlets for immersive collaborative applications. IEEE Pervasive Comput. 12(4), 30–38 (2013)

    Article  Google Scholar 

  27. 5G automotive vision, White Paper, 5GPPP, Oct. (2015). Available https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-White-Paper-on-Automotive-Vertical-Sectors.pdf

  28. Mobile-edge computing—Introductory technical white paper, White Paper, ETSI, Sophia Antipolis, France, Sep. (2014). Available at: https://portal.etsi.org/portals/0/tbpages/mec/docs/mobile-edge_computing_-_introductory_technical_white_paper_v1%2018-09-14.pdf

  29. Ryu, J.-W., et al.: Multi-access edge computing empowered heterogeneous networks: A novel architecture and potential works. Symmetry 11(7), 842 (2019)

    Article  Google Scholar 

  30. Liang, B.: Mobile Edge Computing. Cambridge University Press, New Delhi (2017)

    Google Scholar 

  31. Mobile edge computing use cases & deployment options, White Paper, Juniper, Sunnyvale, (2016). Available at: https://www.juniper.net/assets/us/en/local/pdf/whitepapers/2000642-en.pdf

  32. Cui, G. et al.: Trading off between user coverage and network robustness for edge server placement. IEEE Transactions on Cloud Computing, (2020).

  33. Lahderanta, T., et al.: Edge computing server placement with capacitated location allocation. J. Parall. Distribut. Comput. 153, 130–149 (2021)

    Article  Google Scholar 

  34. Li, Y., Wang, S.: An energy-aware edge server placement algorithm in mobile edge computing. In: 2018 IEEE International Conference on Edge Computing, pp. 66–73, (2018)

  35. Ahmed, A., Ahmed, E.: A survey on mobile edge computing. In: Proceeding of the 10th International Conference Intelligent Systems and Control (ISCO), Coimbatore, (2016), pp. 1–8.

  36. Wang, S., et al.: A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5, 6757–6779 (2017)

    Article  Google Scholar 

  37. Yao, J., et al.: On mobile edge caching. IEEE Commun. Surv. Tutor. 21(3), 2525–2553 (2019)

    Article  Google Scholar 

  38. Islam, A., et al.: A survey on task offloading in multi-access edge computing. J. Syst. Archit. 118, 7 (2021)

    Article  Google Scholar 

  39. Liang, B., et al.: Multi-access Edge Computing fundamentals, services, enablers and challenges: a complete survey. J. Netw. Comput. Appl. 199, 187 (2022)

    Article  Google Scholar 

  40. Shahzadi, S., et al.: Multi-access edge computing: open issues, challenges and future perspectives. J. Cloud Comput. 6(1), 1–13 (2017)

    Article  MathSciNet  Google Scholar 

  41. Qadir, J., et al.: Towards mobile edge computing: taxonomy, challenges, applications and future realms. IEEE Access 8, 189129–189162 (2020)

    Article  Google Scholar 

  42. Porambage, P., et al.: Survey on multi-access edge computing for internet of things realization. IEEE Commun. Surv. Tutor. 20(4), 158 (2018)

    Article  Google Scholar 

  43. Abbas, N., et al.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)

    Article  Google Scholar 

  44. Pham, Q.V., et al.: A survey of multi-access edge computing in 5G and beyond: fundamentals. Technol. Integr. State Art IEEE Access 8, 116974–117017 (2020)

    Article  Google Scholar 

  45. Taleb, T., et al.: On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture & orchestration. IEEE Commun. Surv. Tutor. 19(3), 1657–1681 (2017)

    Article  Google Scholar 

  46. Spinelli, F., Mancuso, V.: Toward enabled industrial verticals in 5G: a survey on MEC-based approaches to provisioning and flexibility. IEEE Commun. Surv. Tutor. 23(1), 596–630 (2021)

    Article  Google Scholar 

  47. Roman, R., et al.: Mobile edge computing, Fog et al. a survey and analysis of security threats and challenges. Future Gener. Comput. Syst. 78, 680–698 (2018)

    Article  Google Scholar 

  48. Shirazi, S.-N., et al.: The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE J. Sel. Areas Commun. 35(11), 2586–2595 (2017)

    Article  Google Scholar 

  49. Jiang, C., et al.: Energy aware edge computing: a survey. Comput. Commun. 151, 556–580 (2020)

    Article  Google Scholar 

  50. Jedari, B., et al.: Video caching analytics, and delivery at the wireless edge: a survey and future directions. IEEE Commun. Surv. Tutorials 23(1), 431–471 (2021)

    Article  Google Scholar 

  51. Shi, Y., et al.: Communication-efficient edge AI: algorithms and systems. IEEE Commun. Surv. Tutor. 22(4), 2167–2191 (2020)

    Article  MathSciNet  Google Scholar 

  52. Wang, S., et al.: Edge server placement in mobile edge computing. J. Parall. Distribut. Comput. 127, 160–168 (2019)

    Article  Google Scholar 

  53. Saputra, Y.M., et al.: Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks. IEEE Wireless Communications Letters 8(4), 1220–1223 (2019)

    Article  Google Scholar 

  54. Kasi, M.K., et al.: Secure mobile edge server placement using multi-agent reinforcement learning. Electronics 10, 171 (2021)

    Article  Google Scholar 

  55. Ling, C. et al.: An edge server placement algorithm based on graph Convolution Network. In: IEEE Transactions on Vehicular Technology, (2022)

  56. Cong, P., et al.: A survey of hierarchical energy optimization for mobile edge computing: a perspective from end devices to the cloud. ACM Comput. Surv. 53(2), 1–44 (2020)

    Google Scholar 

  57. Satyanarayanan, M., et al.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)

    Article  Google Scholar 

  58. Mobile Edge Computing, Springer Science and Business Media LLC, (2021).

  59. Mansouri, Y., et al.: A review of edge computing: Features and resource virtualization. J. Parall. Distrib. Comput. 150, 155–183 (2021)

    Article  Google Scholar 

  60. C. Dou et al., Adhd fmri short-time analysis method for edge computing based on multi-instance learning, J. Syst. Archit., 111 : 101834 (2020).

    Article  Google Scholar 

  61. Wu, C., et al.: Online user allocation in mobile edge computing environments: a decentralized reactive approach. J. Syst. Archit. 113, 156 (2021)

    Article  Google Scholar 

  62. Vaquero, L.-M., Rodero-Merino, L.: Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 44(5), 27–32 (2014)

    Article  Google Scholar 

  63. Ketykó, I. et al.: Multi-user computation offloading as multiple knapsack problem for 5G mobile edge computing. In: Proceedings of the 2016 IEEE EuCNC, IEEE, New York, pp.225–229, (2016)

  64. Jin, X., et al.: A survey on edge computing for wearable technology. Digital Signal Process. (2021). https://doi.org/10.1016/j.dsp.2021.103146

    Article  Google Scholar 

  65. Qiu, T., et al.: Edge computing in industrial internet of things: architecture advances and challenges. IEEE Commun. Surv. Tutor. 22(4), 2462–2488 (2020)

    Article  Google Scholar 

  66. Saeik, F., et al.: Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions. Comput. Netw. 195, 108177 (2021)

    Article  Google Scholar 

  67. Chen, M., Hao, Y.: Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. 36, 587–597 (2018)

    Article  Google Scholar 

  68. Hassan, N., et al.: Edge computing in 5G: a review. IEEE Access 7, 127276–127289 (2019)

    Article  Google Scholar 

  69. Siriwardhana, Y., et al.: A survey on mobile augmented reality with 5G mobile edge computing: architectures applications, and technical aspects. , IEEE Commun. Surv. Tutor. 23(2), 1160–1192 (2021)

    Article  Google Scholar 

  70. ETSI Executive Briefing—Mobile Edge Computing (MEC) Initiative. Available at: https://portal.etsi.org/portals/0/tbpages/mec/docs/mec%20executive%20brief%20v1%2028-09-14.pdf. Accessed 1 Feb 2018

  71. Erol-Kantarci, M., Sukhmani, S.: Caching and computing at the edge for mobile augmented reality and virtual reality (AR/VR) in 5G. In: Ad Hoc Networks, Springer, New York, 169–177, (2018)

  72. Chen, M., et al.: Virtual reality over wireless networks: quality-of-service model and learning-based resource management. IEEE Trans. Commun. 66(11), 5621–5635 (2018)

    Article  Google Scholar 

  73. Bastug, E., et al.: Toward interconnected virtual reality: opportunities, challenges, and enablers. IEEE Commun. Mag. 55(6), 110–117 (2017)

    Article  Google Scholar 

  74. Beck, M.T. et al.: ME-VoLTE: Network functions for energy-efficient video transcoding at the mobile edge. In: Proceeding of the International Conference on Intelligence in Next Generation Networks (ICIN), Paris, (2015), pp. 38–44.

  75. Wang, J. et al.: Elastic urban video surveillance system using edge computing, in: Proceedings of the Workshop on Smart Internet of Things, pp. 1–6, (2017)

  76. Cho, J. et al.: Acacia: context-aware edge computing for continuous interactive applications over mobile networks, In: Proceeding of the 12th ACM International Conference on Emerging Technologies, Irvine, CA, pp. 375–389, (2016)

  77. Truong, N.B. et al.: Software defined networking-based vehicular adhoc network with fog computing, In: Proceeding of the IFIP/IEEE 2021 IFIP IEEE International Symposium on Integrated Network Management (IM), Ottawa, pp. 1202–1207, (2015)

  78. Dahmen-Lhuissier, S.: ETSI-multi-access edge computing-standards for MEC. ETSI (2021). Available at: https://www.etsi.org/technologies/multi-access-edge-computing. Accessed 9 Oct 2021

  79. Wu, H., et al.: A comprehensive review on edge caching from the perspective of total process: placement. Policy Deliv. Sens. 21(15), 5 (2021)

    Google Scholar 

  80. Zhao, L., et al.: Optimal placement of cloudlets for access delay minimization in SDN-based internet of things networks. IEEE Internet Things J. 5(2), 1334–1344 (2018)

    Article  Google Scholar 

  81. Rodrigues, T.K., et al.: Machine learning meets computation and communication control in evolving edge and cloud: challenges and future perspective. IEEE Commun. Surv.Tutor. 22(1), 38–67 (2020)

    Article  Google Scholar 

  82. Kasi, S.K., et al.: Heuristic edge server placement in industrial internet of things and cellular networks. IEEE Internet Things J. 8(13), 10308–10317 (2021)

    Article  Google Scholar 

  83. Santoyo-González, A., Cervelló-Pastor, C.: Edge nodes infrastructure placement parameters for 5G networks, In: 2018 IEEE Conference on Standards for Communications and Networking (CSCN), pp. 1–6, (2018)

  84. Xu, X., et al.: edge server quantification and placement for offloading social media services in industrial cognitive IoV. IEEE Trans. Industr. Inf. 17(4), 2910–2918 (2021)

    Article  Google Scholar 

  85. Shen, B., et al.: Dynamic server placement in edge computing toward Internet of Vehicles. Comput. Commun. 178, 114–123 (2021)

    Article  Google Scholar 

  86. Xu, X. et al.: Load-aware edge server placement for mobile edge computing in 5G networks, In: 17th International Conference on Service-Oriented Computing (ICSOC), pp. 494–507, (2019)

  87. Zhang, J., et al.: Service offloading oriented edge server placement in smart farming. Software 51(12), 2540–2557 (2021)

    Google Scholar 

  88. Zhao, X., et al.: Optimize the placement of edge server between workload balancing and system delay in smart city. Peer-to-Peer Netw. Appl. 14(6), 3778–3792 (2021)

    Article  MathSciNet  Google Scholar 

  89. Jabbari, G., et al.: Heterogenous server placement for delay sensitive applications in green mobile edge computing. Wirel. Personal Commun. 126(2), 1301–1319 (2022)

    Article  Google Scholar 

  90. Li, B., et al.: Placement of edge server based on task overhead in mobile edge computing environment. Trans. Emerging Telecommun. Technol. 32(9), 2 (2021)

    Google Scholar 

  91. Yan, Z. et al.: Exploiting edge computing in internet of space things networks: dynamic and static server placement, In: IEEE Vehicular Technology Conference, (2021).

  92. Cao, K., et al.: Exploring placement of heterogeneous edge servers for response time minimization in mobile edge-cloud computing. IEEE Trans. Industr. Inf. 17(1), 494–503 (2021)

    Article  Google Scholar 

  93. Li, B. et al.: Suitability-based edge server placement strategy in 5G ultra-dense networks, In: 25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD, pp. 1108 – 1113, (2022)

  94. Chen, Y., et al.: Preference-aware edge server placement in the internet of things. IEEE Internet Things J. 9(2), 1289–1299 (2022)

    Article  Google Scholar 

  95. Mehta, A. et al.: How beneficial are intermediate layer data centers in mobile edge networks?. In: International Workshops on Foundations and Applications of Self Systems, pp. 222–229, (2016)

  96. Meurisch, C. et al.: Temporal coverage analysis of router-based cloudlets using human mobility patterns, In: Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM 2017), pp. 1–6, (2017)

  97. Xiao, K. et al.: A heuristic algorithm based on resource requirements forecasting for server placement in edge computing, In: Proceedings of the 2018 IEEE/ACM Symposium on Edge Computing (SEC), pp. 354–355, (2018)

  98. Asghar, A., et al.: Self-Healing in emerging cellular networks: review, challenges, and research directions. IEEE Commun Surv Tutorials 20(3), 1682–1709 (2018)

    Article  Google Scholar 

  99. Hussain, B., et al.: Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks. IEEE Trans. Industr. Inf. 16(8), 4986–4996 (2020)

    Article  Google Scholar 

  100. Ren, Y. et al.: A low-cost edge server placement strategy in wireless metropolitan area networks, In: 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1–6, (2018)

  101. Li, B., et al.: Optimal edge server deployment and allocation strategy in 5G ultra-dense networking environments. Pervasive Mobile Comput. 72, 15 (2021)

    Article  Google Scholar 

  102. Cui, G. et al.: Robustness-oriented k Edge Server Placement In: 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 81–90, (2020)

  103. Wang, Z., et al.: An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing. J. Supercomput. 78(3), 4032–4056 (2022)

    Article  Google Scholar 

  104. Chang, L., et al.: Edge Server Placement for Vehicular Ad Hoc Networks in Metropolitans. IEEE Internet Things J. 9(2), 1575–1590 (2022)

    Article  MathSciNet  Google Scholar 

  105. He, Z., et al.: Cost-efficient server configuration and placement for mobile edge computing. IEEE Trans. Parall. Distribut Syst. 33(9), 2198–2212 (2022)

    Article  Google Scholar 

  106. Zheng, D. et al.: On the placement of edge server for mobile edge computing. In: 2021 7th International Conference on Computer and Communications, ICCC 2021, pp. 1355–1359, (2021)

  107. Li, Y., et al.: Profit-aware edge server placement. IEEE Internet Things J. 9(1), 55–67 (2022)

    Article  MathSciNet  Google Scholar 

  108. Lu, D. et al.: Robust server placement for edge computing, In: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 285–294, (2020)

  109. Service-Oriented Computing, Springer Science and Business Media LLC, (2020).

  110. Ha, K. et al.: The impact of mobile multimedia applications on data center consolidation. In: IEEE International Conference on Cloud Engineering. (IC2E), Redwood, pp. 166–176, (2013)

  111. Barroso, L.-A., Hölzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)

    Article  Google Scholar 

  112. Beloglazov, A., et al.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)

    Article  Google Scholar 

  113. Fan, X. et al.: Power provisioning for a warehouse-sized computer. In: Proceeding of the 34th ACM Annual International Symposium on Computer Architecture (ISCA), San Diego, pp. 13–23, (2007)

  114. Lin, C.-C. et al.: Energy-efficient virtual machine provision algorithms for cloud systems, In: Proceeding IEEE Utility Cloud Computer (UCC), Melbourne, pp. 81–88, (2011)

  115. Ma, L. et al.: Efficient service handoff across edge servers via docker container migration, In: Proceeding of the ACM/IEEE Symposium on Edge Computing (SEC), pp. 1–13, (2017)

  116. Meng, J. et al.: Joint heterogeneous server placement and application configuration in edge computing. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp. 488–497, (2019)

  117. Z. Liu et al., Joint optimization of server placement and content caching in mobile edge computing networks, In: Proceeding of the 8th International Conference on Networks, Communication and Computing (ICNCC), pp. 149–153, (2019)

  118. Gong, Y.: Optimal Edge Server and Service Placement in Mobile Edge Computing. In: 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 688–691, (2020)

  119. Takeda, A. et al.: Joint optimization of edge server and virtual machine placement in edge computing environments, In: 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1545–1548, (2020)

  120. Zhang, X., et al.: Joint edge server placement and service placement in mobile-edge computing. IEEE Internet Things J. 9(13), 11261–11274 (2022)

    Article  Google Scholar 

  121. Li, Y., et al.: Joint placement of UPF and edge server for 6G network. IEEE Internet Things J. 8(22), 16370–16378 (2021)

    Article  Google Scholar 

  122. Gupta, D. et al.: Optimal network design: edge server placement and link capacity assignment for delay-constrained services. In: Proceedings of the 2021 17th international conference on network and service management: smart management for future networks and services, CNSM 2021, pp. 111–117, (2021)

  123. Zhao, S. et al.: Design of robust and efficient edge server placement and server scheduling policies, In: 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), pp. 1–7, (2021)

  124. Hou, P., et al.: Joint hierarchical placement and configuration of edge servers in C-V2X. Ad Hoc Netw. 131, 102842 (2022)

    Article  Google Scholar 

  125. Manasvi, G. et al.: Social network aware dynamic edge server placement for next-generation cellular networks. In: 2020 International Conference on Communication Systems & NETworkS (COMSNETS), pp. 499–502, (2020)

  126. Li, X., et al.: Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks. IET Commun. 14(21), 3907–3916 (2021)

    Article  Google Scholar 

  127. Zhang, X., et al.: An edge server placement method with cyber-physical-social systems in 5G. In: International Conference on Simulation Tools and Techniques, pp. 127–139, (2020)

  128. Abrar, M., et al.: Energy efficient UAV-enabled mobile edge computing for IoT devices: a review. IEEE Access 9, 127779–127798 (2021)

    Article  Google Scholar 

  129. Nouri, N., et al.: 3D Multi-UAV placement and resource allocation for energy-efficient IoT communication. IEEE Internet Things J. 9(3), 2134–2152 (2021)

    Article  Google Scholar 

  130. Huang, X. et al.: A more refined mobile edge cache replacement scheme for adaptive video streaming with mutual cooperation in multi-mec servers. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6, (2020)

  131. Xia, Q., et al.: A survey of federated learning for edge computing: research problems and solutions. High-Conf. Comput. 1(1), 45 (2021)

    Google Scholar 

  132. Moazzeni, S., Khayyambashi, M.R., Movahhedinia, N.: On reliability improvement of software-defined networks. Comput. Netw. 133, 195–211 (2018)

    Article  Google Scholar 

  133. Zeng, F., et al.: Cost-effective edge server placement in wireless metropolitan area networks. Sensors 19(1), 32 (2019)

    Article  MathSciNet  Google Scholar 

  134. Guo, X. et al.: Mobile edge server placement based on bionic swarm intelligent optimization algorithm. In: International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 95–111, (2020)

  135. Hu, Z. et al.: An edge server placement algorithm based on genetic algorithm. In: ACM International Conference Proceeding Series, 2021 ACM Turing Award Celebration Conference China, ACM TURC 2021, pp. 92–97, (2021). https://doi.org/10.1145/3472634.3472658

  136. Qu, Y. et al.: Server placement for edge computing: a robust submodular maximization approach. IEEE Trans. Mobile Comput., (2021).

  137. Zhang, Q., et al.: Cost-aware edge server placement. Int. J. Web Grid Serv. 18(1), 15 (2022)

    Article  MathSciNet  Google Scholar 

  138. Luo, F., et al.: An edge server placement method based on reinforcement learning. Entropy 24, 317 (2022)

    Article  MathSciNet  Google Scholar 

  139. Dash, S. et al.: Clustering based efficient MEC server placement and association in 5G networks. In: 2021 19th OITS International Conference on Information Technology (OCIT), (2021), pp. 167–172.

  140. Wang, F. et al.: Cost-effective edge server placement in edge computing, In: 5th International Conference on Systems, Control and Communications, ICSCC 2019, pp. 6–10, (2019)

  141. Ke, Y.: Bipartite Graph based Edge Server Placement Algorithm in Mobile Edge Computing, In: 2021 International Conference on Electronic Information Engineering and Computer Science, EIECS 2021, pp. 493-496, (2021)

  142. Lu, J., et al.: Deep reinforcement learning-based multi-objective edge server placement in Internet of Vehicles. Comput. Commun. 187, 172–180 (2022)

    Article  Google Scholar 

  143. Takeda, A., et al.: Evaluation of edge cloud server placement for edge computing environments, In: 6th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2019, (2019).

  144. Chen, X. et al.: An edge server placement algorithm in edge computing environment, In: 12th International Conference on Advanced Infocomm Technology, ICAIT 2020, (2020).

  145. Guo, F., et al.: Mobile edge server placement based on meta-heuristic algorithm. Journal of Intelligent and Fuzzy Systems 40(5), 8883–8897 (2021)

    Article  Google Scholar 

  146. Ma, R.: Edge server placement for service offloading in internet of things. Securi. Commun. Netw. 22, 1–16 (2021)

    Google Scholar 

  147. Wang, L., et al.: SCESP: An edge server placement method based on spectral clustering in mobile edge computing. In: International Conference on Artificial Intelligence and Security, pp. 527–539, (2022)

  148. Carvalho, D., et al.: Edge servers placement in mobile edge computing using stochastic Petri nets. Int. J. Comput. Sci. Eng. 23(4), 352–366 (2020)

    Google Scholar 

  149. Huang, P.-C., et al.: Server placement and task allocation for load balancing in edge-computing networks. IEEE Access 9, 138200–138208 (2021)

    Article  Google Scholar 

  150. Zhang, J., et al.: Quantified edge server placement with quantum encoding in internet of vehicles. IEEE Trans. Intell. Transp. Syst. (2021). https://doi.org/10.1109/TITS.2021.3116960

    Article  Google Scholar 

  151. Kostakos, V., et al.: Traffic in the smart city: exploring citywide sensing for traffic control center augmentation. IEEE Internet Comput. 17(6), 22–29 (2013)

    Article  Google Scholar 

  152. Lai, P. et al.: Optimal edge user allocation in edge computing with variable sized vector bin packing, In: International Conference on Service-Oriented Computing, Springer, pp. 230–245, (2018)

  153. GitHub. GitHub - swinedge/eua-dataset: Edge server, user dataset for Edge Computing research. Available at: https://github.com/swinedge/eua-dataset. Accessed 22 Dec 2021

  154. Telecom, Shanghai, China. The distribution of 3233 Base Stations. Available at: https://sguangwang.com/TelecomDataset.html. Accessed 18 May 2023

  155. Huang, H., et al.: A metropolitan taxi mobility model from real GPS traces. J. Univ. Comput. Sci. 18(9), 89 (2012)

    Google Scholar 

  156. Mohan, N. et al.: Anveshak: Placing edge servers in the wild, In: Proceeding of the Workshop Mobile Edge Communication (MECOMM), New York, pp. 7–12, (2018)

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Bahareh Bahrami: Conceptualization, Methodology, Writing—original draft, Writing – review & editing, Visualization. Mohammad Reza Khayyambashi: Conceptualization, Methodology, Writing—review & editing, Supervision. Seyedali Mirjalili: Conceptualization, Methodology, Writing—review & editing, Supervision.

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Bahrami, B., Khayyambashi, M.R. & Mirjalili, S. Edge server placement problem in multi-access edge computing environment: models, techniques, and applications. Cluster Comput 26, 3237–3262 (2023). https://doi.org/10.1007/s10586-023-04025-7

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