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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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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 et al.
In: IEEE Transactions on Green Communications and Networking, Vol. 7, No. 4, 01.12.2023, p. 1667 - 1677.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

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, vol. 7, no. 4, pp. 1667 - 1677. https://doi.org/10.1109/TGCN.2023.3283509

APA

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

Vancouver

Liu Q, Zeng L, Bilal M, Song H, Liu X, Zhang Y et al. A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. IEEE Transactions on Green Communications and Networking. 2023 Dec 1;7(4):1667 - 1677. Epub 2023 Jun 7. doi: 10.1109/TGCN.2023.3283509

Author

Liu, Qi ; Zeng, Lei ; Bilal, Muhammad et al. / A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. In: IEEE Transactions on Green Communications and Networking. 2023 ; Vol. 7, No. 4. pp. 1667 - 1677.

Bibtex

@article{17f084ed6e6c4ce395633196f44198ce,
title = "A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter",
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.",
keywords = "Datacenter Management, Dynamic scheduling, Heuristic algorithms, Job shop scheduling, Load Balancing, Optimal scheduling, Particle Swarm Optimization, Particle swarm optimization, Supply Chain, Supply chains, Sustainable Task Scheduling, Task analysis",
author = "Qi Liu and Lei Zeng and Muhammad Bilal and Houbing Song and Xiaodong Liu and Yonghong Zhang and Xuefei Cao",
year = "2023",
month = dec,
day = "1",
doi = "10.1109/TGCN.2023.3283509",
language = "English",
volume = "7",
pages = "1667 -- 1677",
journal = "IEEE Transactions on Green Communications and Networking",
issn = "2473-2400",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

AU - Liu, Qi

AU - Zeng, Lei

AU - Bilal, Muhammad

AU - Song, Houbing

AU - Liu, Xiaodong

AU - Zhang, Yonghong

AU - Cao, Xuefei

PY - 2023/12/1

Y1 - 2023/12/1

N2 - 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.

AB - 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.

KW - Datacenter Management

KW - Dynamic scheduling

KW - Heuristic algorithms

KW - Job shop scheduling

KW - Load Balancing

KW - Optimal scheduling

KW - Particle Swarm Optimization

KW - Particle swarm optimization

KW - Supply Chain

KW - Supply chains

KW - Sustainable Task Scheduling

KW - Task analysis

U2 - 10.1109/TGCN.2023.3283509

DO - 10.1109/TGCN.2023.3283509

M3 - Journal article

VL - 7

SP - 1667

EP - 1677

JO - IEEE Transactions on Green Communications and Networking

JF - IEEE Transactions on Green Communications and Networking

SN - 2473-2400

IS - 4

ER -