Title: Load balancing algorithm based on multiple linear regression analysis in multi-agent systems

Authors: Dong-sheng Liu; Xiao-hong Xiao; Xiao-hui Zeng

Addresses: School of Electronic and Information Engineering, Jinggangshan University, China; Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, China ' School of Electronic and Information Engineering, Jinggangshan University, China; Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, China ' School of Electronic and Information Engineering, Jinggangshan University, China; Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, China; The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, China

Abstract: With the increase of agents involved in applications of multi-agent systems (MASs), the problem of load balancing is more and more prominent. This paper proposes a novel load balancing algorithm based on multiple linear regression analysis (LBAMLR). By using parallel computing on all servers and utilising partial information about agents' communication, our algorithm can effectively chose the optimal agents set and the suitable destination servers. The simulation results show our proposed algorithm can shorten the computing time and increase the total performance in MAS.

Keywords: distributed computing; multi-agent systems; MASs; load balancing; multiple linear regression analysis.

DOI: 10.1504/IJCSE.2018.091774

International Journal of Computational Science and Engineering, 2018 Vol.16 No.3, pp.234 - 241

Received: 10 Feb 2016
Accepted: 20 Aug 2016
Published online: 16 May 2018 *

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