A Two-Way Update Resource Allocation Strategy in Mobile Edge Computing
Authors: 
Zhe Yang, Yi-peng Chen, Fei Gu, Jin Wang, Lei Zhao Academic Editor: Kalidoss RajakaniAuthors Info & Claims
Zhe Yang, Yi-peng Chen, Fei Gu, Jin Wang, Lei Zhao Academic Editor: Kalidoss RajakaniAuthors Info & ClaimsAbstract
Existing studies of mobile edge computing resource allocation strategy problem merely optimize delay and energy cost, seldom considering the benefit of edge servers. So, a two-way update strategy based on game theory (TUSGT) was proposed. TUSGT converts the task competition relationship among edge servers into a noncollaborative game issue and adopts a potential game-based joint optimization strategy, allowing edge servers to determine task selection preference by maximizing their own benefit as the objective. At mobile device side, the EWA algorithm of online learning was used to update parameters, exerting impact on edge server’s task selection preference from a global perspective and improving overall deadline hit rate. The simulation test results show that, compared to BGTA, MILP, greedy strategy, random strategy, and ideal strategy, TUSGT promotes deadline hit rate by up to 30% and increases edge server’s average benefit by up to 65%.
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Copyright © 2022 Zhe Yang et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Published: 01 January 2022
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