Interference-aware SaaS user allocation game for Edge Computing

Guangming Cui, Qiang He, Xiaoyu Xia, Phu Lai, Feifei Chen, Tao Gu, Yun Yang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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 paper, 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.

Original languageEnglish
Pages (from-to)SJR Q1
JournalIEEE Transactions on Cloud Computing
Early online date10 Jul 2020
DOIs
Publication statusE-pub ahead of print - 10 Jul 2020
Externally publishedYes

Keywords

  • Data rate
  • Edge computing
  • Game theory
  • Interference
  • Nash Equilibrium
  • Potential game
  • SaaS user allocation

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