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<p>Efficient task scheduling in Cloud Computing remains an NP-hard challenge due to combinatorial search spaces and resource heterogeneity, often leading to premature convergence in existing metaheuristics. This paper proposes FL-Jaya, an enhanced Jaya algorithm that addresses these limitations through two key innovations: (1) a Fitness-Distance Balance (FDB) mechanism, which preserves population diversity by selecting solutions that optimally trade off fitness quality and spatial distribution, and (2) a Lévy Flight (LF) operator, enabling stochastic long jumps to escape local optima. By unifying FDB and LF into a single update rule, FL-Jaya dynamically balances exploration and exploitation, overcoming stagnation in large-scale scheduling. Experiments on artificial (100–1000 tasks) and real-world Google Cloud Jobs datasets demonstrate FL-Jaya’s superiority over six algorithms—Jaya, Particle Swarm Optimization, Coati Optimization Algorithm, Whale Optimization Algorithm, Bald Eagle Search, and Snake Optimizer. FL-Jaya achieves 38.98% lower makespan and 44.63% higher average resource utilization (ARU) than standard Jaya on artificial workloads, with real-world results showing 35.34% makespan reduction and 44.63% ARU improvement. These gains stem from FL-Jaya’s ability to maintain solution diversity while navigating complex search spaces, outperforming peers in convergence speed and scalability. The algorithm’s parameter-light design and consistent performance underscore its practicality for heterogeneous cloud environments.</p>

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FL-Jaya scheduler: an optimized Jaya algorithm for effective independent task scheduling in cloud systems

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Husam S. Mashaqbeh,
  • Salameh A. Mjlae

摘要

Efficient task scheduling in Cloud Computing remains an NP-hard challenge due to combinatorial search spaces and resource heterogeneity, often leading to premature convergence in existing metaheuristics. This paper proposes FL-Jaya, an enhanced Jaya algorithm that addresses these limitations through two key innovations: (1) a Fitness-Distance Balance (FDB) mechanism, which preserves population diversity by selecting solutions that optimally trade off fitness quality and spatial distribution, and (2) a Lévy Flight (LF) operator, enabling stochastic long jumps to escape local optima. By unifying FDB and LF into a single update rule, FL-Jaya dynamically balances exploration and exploitation, overcoming stagnation in large-scale scheduling. Experiments on artificial (100–1000 tasks) and real-world Google Cloud Jobs datasets demonstrate FL-Jaya’s superiority over six algorithms—Jaya, Particle Swarm Optimization, Coati Optimization Algorithm, Whale Optimization Algorithm, Bald Eagle Search, and Snake Optimizer. FL-Jaya achieves 38.98% lower makespan and 44.63% higher average resource utilization (ARU) than standard Jaya on artificial workloads, with real-world results showing 35.34% makespan reduction and 44.63% ARU improvement. These gains stem from FL-Jaya’s ability to maintain solution diversity while navigating complex search spaces, outperforming peers in convergence speed and scalability. The algorithm’s parameter-light design and consistent performance underscore its practicality for heterogeneous cloud environments.