Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing

• Published in: Sustainable computing informatics and systems Vol. 43; p. 101012
• Main Authors: Abualigah, Laith, Hussein, Ahmad MohdAziz, Almomani, Mohammad H., Zitar, Raed Abu, Migdady, Hazem, Alzahrani, Ahmed Ibrahim, Alwadain, Ayed
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2024; Elsevier
• Subjects: Cloud Computing; Computer Science; Jaya Algorithm; Levy Flight Mechanism; Resource Utilization; Synergistic Swarm Optimization; Task Scheduling; Levy Flight Mechanism; Cloud Computing; Resource Utilization; Task Scheduling; Synergistic Swarm Optimization; Jaya Algorithm
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2024.101012

Cloud computing has emerged as a cornerstone technology for modern computational paradigms due to its scalability and flexibility. One critical aspect of cloud computing is efficient task scheduling, which directly impacts system performance and resource utilization. In this paper, we propose an enhanced optimization algorithm tailored for task scheduling in cloud environments. Building upon the foundation of the Jaya algorithm and Synergistic Swarm Optimization (SSO), our approach integrates a Levy flight mechanism to enhance exploration-exploitation trade-offs and improve convergence speed. The Jaya algorithm's ability to exploit the current best solutions is complemented by the SSO's collaborative search strategy, resulting in a synergistic optimization framework. Moreover, the incorporation of Levy flights injects stochasticity into the search process, enabling the algorithm to escape local optima and navigate complex solution spaces more effectively. We evaluate the proposed algorithm against state-of-the-art approaches using benchmark task scheduling problems in cloud environments. Experimental results demonstrate the superiority of our method in terms of solution quality, convergence speed, and scalability. Overall, our proposed Improved Jaya Synergistic Swarm Optimization Algorithm offers a promising solution for optimizing TSCC (TSCC), contributing to enhanced resource utilization and system performance in cloud-based applications. The proposed method got 88 % accuracy overall and 10 % enhancement compared to the original method. •JSSOA optimizes cloud task scheduling by integrating fitness-distance balance and Lévy flight.•Efficient cloud task scheduling is achieved with JSSOA.•JSSOA excels in empirical evaluations, outperforming Jaya and SSOA algorithms.•JSSOA showcases its prowess in advancing cloud computing efficiency.