Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221200150-8.doi: 10.11896/jsjkx.221200150

• Network & Communication • Previous Articles     Next Articles

Multi-edge Server Load Balancing Strategy Based on Game Theory

WENG Jie1,2, LIN Bing2,3, CHEN Xing1,2   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350108,China
    3 College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China
  • Published:2023-11-09
  • About author:WENG Jie,born in 1999,postgraduate,is a member of China Computer Federation.His main research interests include cloud computing and edge computing and game theory.
    LIN Bing,born in 1986,Ph.D,associate professor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include cloud computing and intelligent computing and its application.
  • Supported by:
    National Natural Science Foundation of China(62072108) and University-Industry Cooperation of Fujian Pro-vince(2022H6024).

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Abstract: As an emerging computing paradigm,mobile edge computing aims to make up for the shortage of computing,storage and bandwidth of mobile devices in the Internet of Things.Due to geographical and time factors,the load of edge servers varies greatly,so the load balancing of multi-edge servers is very important.This paper proposes a multi-edge server load balancing strategy based on game theory to meet the load balancing requirements among edge servers.Firstly,the MEC server load balancing problem is modeled as a non-cooperative game,and the unique Nash equilibrium solution is obtained by introducing the regularization method based on proximal decomposition algorithm.Then,according to the established game model,a distributed load balancing algorithm(DLBA) is proposed to optimize the system response time and energy consumption.Experimental results show that DLBA can quickly reach Nash equilibrium with fewer iterations.Compared with the local computing strategy and computing power allocation strategy,the average response delay of DLBA strategy is reduced by 18.39% and 9.91%,and the average energy consumption is reduced by 2.42% and 7.33%.The gap between DLBA and the optimal strategy obtained by particle swarm optimization genetic algorithm is small,but the computation time is only 1.81% of that of particle swarm optimization genetic algorithm.Therefore,the proposed strategy can effectively reduce the system response time and energy consumption,and the execution speed is fast,which is applicable to real scenarios.

Key words: Mobile edge computing, Game theory, Non-cooperative game, Distributed, Load balancing

CLC Number: 

  • TP393
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