Journal
IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 13, Issue 4, Pages 723-734Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2020.2966196
Keywords
Resource management; Games; Edge computing; Quality of experience; Computational modeling; Numerical models; Approximation algorithms; Edge computing; resource allocation; game theory
Funding
- General Research Fund of the Research Grants Council of Hong Kong [PolyU 152221/19E]
- National Natural Science Foundation of China [61872310, 61832006, 61872240, 61872195, 61532013, 61872239]
- DCT-MoST Joint-project of Science and Technology Development Fund, Macao S.A.R (FDCT), China [025/2015/AMJ]
- University of Macau [MYRG2018-00237-RTO, CPG2019-00004-FST, SRG2018-00111-FST]
- [0007/2018/A1]
- [0060/2019/A1]
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Existing works adopt the Edge-Oriented Resource Allocation (EORA) scheme, in which edge nodes cache services and schedule user requests to distribute workloads over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, EORA does not fully take into account the fact that service providers are sometimes independent from the edge operators with their own objectives. To deal with the conflict and cooperation between service providers and edge nodes, we devise a service-oriented resource allocation (SORA) scheme, where edge nodes and service providers adjust their resource allocations to provide requested services. We first prove that such resource allocation problem is NP-hard. We then propose a three-sided cyclic game (3CG) involving users, edge nodes, and service providers who make their individual decisions by choosing respectively high-quality services, high-value users, and cost-effective edge nodes for service deployment. Based on 3CG, we prove the existence and approximation ratio of pure-strategy Nash equilibriums (NEs). We also develop both centralized and distributed approximate algorithms for resource allocation. Finally, extensive experimental results validate the effectiveness and convergence of the proposed algorithms.
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