Abstract
Mobile Edge Computing (MEC) policies that bind user service requests to edge servers, seldom take into account user preferences of Quality-of-Service (QoS) and the resulting Quality-of-Experience (QoE). In this paper, we design a novel user-centric optimal allocation policy considering the QoS preferences of users, with an attempt to maximize the overall QoE. Additionally, we propose a real-time mobility aware user-centric heuristic algorithm to solve the allocation problem by accommodating the time varying QoS demands of users. Experimental results on real data sets demonstrate the efficiency of our allocation scheme and a comparison with state-of-art approaches in MEC literature.
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Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: MCC, pp. 13–16. ACM (2012)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)
Guo, H., Liu, J., Qin, H.: Collaborative mobile edge computation offloading for IoT over fiber-wireless networks. IEEE Network 32(1), 66–71 (2018)
He, Q., et al.: A game-theoretical approach for user allocation in edge computing environment. IEEE TPDS 31, 515–529 (2020)
Lai, P., et al.: Optimal edge user allocation in edge computing with variable sized vector bin packing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 230–245. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_15
Lai, P., et al.: Edge user allocation with dynamic quality of service. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds.) ICSOC 2019. LNCS, vol. 11895, pp. 86–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33702-5_8
Peng, Q., et al.: Mobility-aware and migration-enabled online edge user allocation in mobile edge computing. In: ICWS, pp. 91–98 (2019)
Wang, C., Liang, C., Yu, F.R., Chen, Q., Tang, L.: Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE TWC 16(8), 4924–4938 (2017)
Wang, S., Guo, Y., Zhang, N., Yang, P., Zhou, A., Shen, X.S.: Delay-aware microservice coordination in mobile edge computing: a reinforcement learning approach. IEEE TMC, pp. 1–1 (2019)
Zhu, R., Liu, B., Niu, D., Li, Z., Zhao, H.V.: Network latency estimation for personal devices: a matrix completion approach. IEEE/ACM ToN 25(2), 724–737 (2016)
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Panda, S.P., Ray, K., Banerjee, A. (2020). Dynamic Edge User Allocation with User Specified QoS Preferences. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_15
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DOI: https://doi.org/10.1007/978-3-030-65310-1_15
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