Computer Science ›› 2019, Vol. 46 ›› Issue (8): 163-170.doi: 10.11896/j.issn.1002-137X.2019.08.027

• Network & Communication • Previous Articles     Next Articles

Cloudlet Workload Balancing Algorithm in Wireless Metropolitan Area Networks

ZENG Jin-jing1,2, ZHANG Jian-shan2,3, LIN Bing2,3, ZHANG Wen-de1   

  1. (School of Economics and Management,Fuzhou University,Fuzhou 350116,China)1
    (Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China)2
    (College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China)3
  • Received:2019-03-26 Online:2019-08-15 Published:2019-08-15

Abstract: With the development of wireless communication technology,more and more business,entertainments and social activities are built on portable mobile devices.The size of portable mobile devices limits their computing power,and the lack of computing power conflicts with the high computational requirement of the application.Edge computing enables computational tasks to be processed in time near the source,which is an effective way to reduce system delay.Cloudlet technology is an important application of edge computing,and deploying cloudlet is an effective way to solve the above contradiction.Multiple cloudlet are connected to form a network,and end-user can get cloudlet services via wireless metropolitan area network(WMAN).The currently major challenges are how to offload and schedule the tasksto a reasonable cloudlet to reduce system delay.This paper investigated how to balance the workload between multiple cloudlet in a network to optimize the performance of mobile applications.It first introduced a system model to get the response times of offloaded tasks,and developed an optimal solution finding the best offloading scheme of the task between cloudlet to minimize the average responses time at cloudlets.Then,it proposed a fast,scalable heuristic algorithm for this problem to reduce the user task response time.Finally,it evaluated the performance of the proposed algorithm through experimental simulation.Experimental results show that the algorithm has a positive effect on reducing task response time and improving user experience

Key words: Cloudlet workload balancing, Edge computing, Task re-offloading, Task scheduling

CLC Number: 

  • TP338
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