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<p>Cloud and fog computing architectures are essential for decentralizing computational tasks, enabling efficient processing across user demands and extensive IoT networks. As computational loads grow in complexity and scale, effective task scheduling across these architectures becomes critical for optimizing resource usage and reducing latency. This paper introduces an adapted version of the Marine Predators Algorithm (MPA), a nature-inspired optimization approach to address the task scheduling challenge within cloud and fog environments. Due to the complexity and high-dimensionality of the scheduling problem, MPA was tested on both synthetic and real-world workloads, including HPC2N, NASA iPSC, and GOCJ datasets. Simulation results indicate that MPA outperforms several existing algorithms, achieving significant reductions in average makespan times and enhancing the Degree of Imbalancing, thereby ensuring more efficient resource distribution. PIR values indicate that MPA could achieve better average makespan results by up to 68.28% and by up to 71.06% for average DI results. These findings underscore MPA’s effectiveness in addressing the demanding task scheduling requirements of decentralized computing environments, positioning it as a promising solution for improved performance and resource utilization in cloud and fog infrastructures.</p>

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Mitigating the task scheduling Problem in fog computing environments using marine predators optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Khaled Houssam Mahfouz,
  • Mohammed Azmi Al-Betar,
  • Sharif Naser Makhadmeh,
  • Qusai Yousef Shambour

摘要

Cloud and fog computing architectures are essential for decentralizing computational tasks, enabling efficient processing across user demands and extensive IoT networks. As computational loads grow in complexity and scale, effective task scheduling across these architectures becomes critical for optimizing resource usage and reducing latency. This paper introduces an adapted version of the Marine Predators Algorithm (MPA), a nature-inspired optimization approach to address the task scheduling challenge within cloud and fog environments. Due to the complexity and high-dimensionality of the scheduling problem, MPA was tested on both synthetic and real-world workloads, including HPC2N, NASA iPSC, and GOCJ datasets. Simulation results indicate that MPA outperforms several existing algorithms, achieving significant reductions in average makespan times and enhancing the Degree of Imbalancing, thereby ensuring more efficient resource distribution. PIR values indicate that MPA could achieve better average makespan results by up to 68.28% and by up to 71.06% for average DI results. These findings underscore MPA’s effectiveness in addressing the demanding task scheduling requirements of decentralized computing environments, positioning it as a promising solution for improved performance and resource utilization in cloud and fog infrastructures.