Interference-Aware SaaS User Allocation Game for Edge Computing

Article


Cui, Guangming, He, Qiang, Xia, Xiaoyu, Lai, Phu, Chen, Feifei, Gu, Tao and Yang, Yun. 2022. "Interference-Aware SaaS User Allocation Game for Edge Computing." IEEE Transactions on Cloud Computing. 10 (3), pp. 1888-1899. https://doi.org/10.1109/TCC.2020.3008448
Article Title

Interference-Aware SaaS User Allocation Game for Edge Computing

ERA Journal ID210574
Article CategoryArticle
AuthorsCui, Guangming (Author), He, Qiang (Author), Xia, Xiaoyu (Author), Lai, Phu (Author), Chen, Feifei (Author), Gu, Tao (Author) and Yang, Yun (Author)
Journal TitleIEEE Transactions on Cloud Computing
Journal Citation10 (3), pp. 1888-1899
Number of Pages12
Year2022
Place of PublicationUnited States
ISSN2168-7161
Digital Object Identifier (DOI)https://doi.org/10.1109/TCC.2020.3008448
Web Address (URL)https://ieeexplore.ieee.org/document/9138723
Abstract

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor’s perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users’ data rates. In this article, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

KeywordsData rate; Edge computing; Game theory; Interference; Nash Equilibrium; Potential game; SaaS user allocation
ANZSRC Field of Research 2020460601. Cloud computing
460612. Service oriented computing
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Byline AffiliationsSwinburne University of Technology
Deakin University
Royal Melbourne Institute of Technology (RMIT)
Institution of OriginUniversity of Southern Queensland
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