Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 456-463.doi: 10.11896/jsjkx.210100191

• Network & Communication • Previous Articles     Next Articles

PSO-GA Based Approach to Multi-edge Load Balancing

YAO Ze-wei, LIU Jia-wen, HU Jun-qin, CHEN Xing   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China
    Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University),Fuzhou 350108,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:YAO Ze-wei,born in 1998,postgraduate.His main research interests include computation offloading,computational intelligence and its applications.
    LIN Jia-wen,born in 1985,Ph.D,lecturer,postgraduate supervisor,is a member of CCF.Her main research interests include intelligent information processing,computer vision and medical image analysis.
  • Supported by:
    National Natural Science Foundation of China(62072108),Natural Science Foundation of Fujian Province for Distinguished Young Scholars(2020J06014) and Natural Science Foundation of Fujian Province(2018J07005).

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Abstract: As a new paradigm,mobile edge computing (MEC) can provide an efficient method to solve the computing and storage resource constraints of mobile devices.Through the wireless network,it migrates the intensive tasks on mobile devices to the edges near the users for execution,then the edges transmit the execution results back to mobile devices.Due to the randomness of users' movement,the load on each edge which deployed in the city is usually inconsistent.To solve the problem of multi-edge load balancing,the task scheduling is considered to minimize the maximum response time of tasks in the edge set,thereby improving the performance of mobile devices.Firstly,the multi-edge load balancing problem is formally defined.Then particle swarm optimization-genetic algorithm(PSO-GA) is proposed to solve the multi-edge load balancing problem.Finally,the performance of the algorithm is compared and analyzed with the random migration algorithm and the greedy algorithm through simulation experiments.The experimental results show that PSO-GA is superior to random migration and greedy algorithm by 51.58% and 26.34%,respectively.Therefore,PSO-GA has a better potential for reducing task response time of the edges and improving user experience.

Key words: Load balancing, Mobile edge computing, Particle swarm optimization-genetic algorithm, Task response time

CLC Number: 

  • TP301
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