Open Access

ARTICLE

Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

Jeng-Shyang Pan^1,2, Na Yu¹, Shu-Chuan Chu^1,*, An-Ning Zhang¹, Bin Yan³, Junzo Watada⁴

1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
2 Department of Information Management, Chaoyang University of Technology, Taichung, 41349, Taiwan
3 College of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao, 266590, China
4 Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, 808-0135, Japan

* Corresponding Author: Shu-Chuan Chu. Email:

Computers, Materials & Continua 2025, 82(2), 2495-2520. https://doi.org/10.32604/cmc.2024.058450

Received 12 September 2024; Accepted 20 November 2024; Issue published 17 February 2025

Abstract

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

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Keywords

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