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Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Shasha Zhao1,2,3,*, Huanwen Yan1,2, Qifeng Lin1,2, Xiangnan Feng1,2, He Chen1,2, Dengyin Zhang1,2

1 College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
2 Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
3 Tongding Interconnection Information Co., Ltd., Suzhou, 215000, China

* Corresponding Author: Shasha Zhao. Email: email

Computers, Materials & Continua 2024, 78(1), 1135-1156. https://doi.org/10.32604/cmc.2024.045660

Abstract

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing, and operational costs has been done. The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm. Compared with the Particle Swarm Optimization algorithm, the HPSO algorithm not only improves the global search capability but also effectively mitigates getting stuck in local optima. As a result, the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed. Moreover, it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments, effectively reducing execution time and cost, which also is verified by the ablation experimental.

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Cite This Article

S. Zhao, H. Yan, Q. Lin, X. Feng, H. Chen et al., "Hybrid hierarchical particle swarm optimization with evolutionary artificial bee colony algorithm for task scheduling in cloud computing," Computers, Materials & Continua, vol. 78, no.1, pp. 1135–1156, 2024.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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