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<p>Cloud computing provides flexible, <?tk 2?>on-demand access to a shared pool of computing resources, making task scheduling critical for optimal resource utilization. <?tk 2?>Lack of effective scheduling means that many resources may be used without achieving the intended outcomes since they are costly. <?tk 2?>Although many existing scheduling algorithms have been proposed, <?tk 2?>these algorithms do not guarantee the desired goals in the context of resource utilization. <?tk 2?>In order to mitigate this challenge, we introduce a new metaheuristic approach, <?tk 2?>WPUR-GAGSA, incorporating a <?tk 2?>new WPUR method for ordering the tasks and a GA-GSA for scheduling the tasks. <?tk 2?>The proposed method combines GA for exploitation and GSA for exploration to provide a better solution convergence rate. Compared to the state-of-the-art methods, <?tk 2?>the simulation outcomes report that the suggested WPUR-GAGSA method provides enhancements of 37% makespan, <?tk 2?>41% energy consumption, and 30% cost. <?tk 2?>Later, statistical studies employing one-way ANOVA further reassured and validated <?tk 2?>the reliability and efficiency of the proposed method.</p>

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Efficient soft computing approach for task scheduling in cloud computing

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Sahani Pooja Jaiprakash,
  • Tapas Badal,
  • Naween Kumar

摘要

Cloud computing provides flexible, on-demand access to a shared pool of computing resources, making task scheduling critical for optimal resource utilization. Lack of effective scheduling means that many resources may be used without achieving the intended outcomes since they are costly. Although many existing scheduling algorithms have been proposed, these algorithms do not guarantee the desired goals in the context of resource utilization. In order to mitigate this challenge, we introduce a new metaheuristic approach, WPUR-GAGSA, incorporating a new WPUR method for ordering the tasks and a GA-GSA for scheduling the tasks. The proposed method combines GA for exploitation and GSA for exploration to provide a better solution convergence rate. Compared to the state-of-the-art methods, the simulation outcomes report that the suggested WPUR-GAGSA method provides enhancements of 37% makespan, 41% energy consumption, and 30% cost. Later, statistical studies employing one-way ANOVA further reassured and validated the reliability and efficiency of the proposed method.