多群落粒子群优化供应链数据中心任务调度

doi:10.3969/j.issn.0255-8297.2023.03.005

Abstract

Abstract: In order to deal with the problems of low service efficiency brought by the increasing scale of datacenters and task demands, a load balancing multi-swarm PSO task scheduling approach is proposed. Through the improved fitness function, the maximum completion time of the task and the variance of the completion time among machines are optimized to improve the cluster’s load balance. A novel adaptive inertia weights method is designed to enhance particle search efficiency and algorithm convergence speed. Meanwhile, a new particle initialization method is adopted to improve the quality and diversity of the initial solution. Multi-swarm particle collaborative search is further used to bring the final result closer to the optimal solution. The performance of the proposed algorithm is verified and compared with others based on the public dataset of Alibaba datacenter. The experimental results show that the method can improve task scheduling efficiency of datacenters in diversified supply chain environments.

Key words: particle swarm optimization, supply chain management, datacenter, task scheduling, load balancing

CLC Number:

TP393.09

ZENG Lei, BAI Jinming, LIU Qi. Multi-swarm Particle Swarm Optimization for Task Scheduling in Supply Chain Datacenter[J]. Journal of Applied Sciences, 2023, 41(3): 419-430.

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Multi-swarm Particle Swarm Optimization for Task Scheduling in Supply Chain Datacenter

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