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A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Liu, Qi; Zeng, Lei; Bilal, Muhammad; Song, Houbing; Liu, Xiaodong; Zhang, Yonghong; Cao, Xuefei

Authors

Qi Liu

Lei Zeng

Muhammad Bilal

Houbing Song

Yonghong Zhang

Xuefei Cao



Abstract

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic support for intelligent applications. However, the increase in the number and complexity of tasks makes datacenters face increasingly heavy task processing demands. Therefore, there are problems of long task completion time and long response time in the task scheduling process of the data center. A multi-swarm particle swarm optimization task scheduling approach based on load balancing is proposed in this paper, aiming to reduce the maximum completion time and response time in task scheduling. The proposed approach improves the fitness evaluation function of particle swarms to facilitate load balancing. The new adaptive inertia weight and initialization method design can improve the search efficiency and convergence speed of particles. Meanwhile, the multi-swarm design can avoid the problem of particles falling into local optimum as much as possible. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the proposed algorithm can improve the task scheduling performance of the datacenter in supply chain management when dealing with different workloads and changes in the number of elastic machines.

Citation

Liu, Q., Zeng, L., Bilal, M., Song, H., Liu, X., Zhang, Y., & Cao, X. (2023). A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. IEEE Transactions on Green Communications and Networking, 7(4), 1667 - 1677. https://doi.org/10.1109/tgcn.2023.3283509

Journal Article Type Article
Acceptance Date May 28, 2023
Online Publication Date Jun 7, 2023
Publication Date 2023-12
Deposit Date Jun 9, 2023
Publicly Available Date Jun 9, 2023
Journal IEEE Transactions on Green Communications and Networking
Print ISSN 2473-2400
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Issue 4
Pages 1667 - 1677
DOI https://doi.org/10.1109/tgcn.2023.3283509
Keywords Particle Swarm Optimization, Supply Chain, Datacenter Management, Sustainable Task Scheduling, Load Balancing
Public URL http://researchrepository.napier.ac.uk/Output/3121128

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