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<p>This paper proposes a novel QoS-aware task scheduling approach in cloud computing environments that utilizes the Modified Wombat Optimization Algorithm (MWOA). Task scheduling is a critical challenge in cloud computing. In the Internet of Things (IoT) applications, there is a significant need to identify efficient methods for allocating computational resources or reducing cost and latency while maintaining the reliability of services. Most existing scheduling algorithms encounter difficulties in terms of premature convergence and suboptimal performance in multi-objective scenarios. MWOA addresses this challenge by incorporating Levy flight for enhanced global exploration and a chaotic sine map for local exploitation, thus achieving a delicate balance between convergence speed and solution accuracy. The key QoS factors optimized in this study include the task completion time, execution cost, and resource consumption. Simulation results show that MWOA reduces task completion time by 31% and execution cost by 17% compared to traditional algorithms. In this work, we identify the capabilities of MWOA for future development in the task-scheduling model toward unifying dynamic, computationally intensive cloud, heterogeneous, and real-time IoT environments.</p>

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A novel quality of service-aware task scheduling approach for cloud computing using modified Wombat Optimization Algorithm

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  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Lijuan Yan

摘要

This paper proposes a novel QoS-aware task scheduling approach in cloud computing environments that utilizes the Modified Wombat Optimization Algorithm (MWOA). Task scheduling is a critical challenge in cloud computing. In the Internet of Things (IoT) applications, there is a significant need to identify efficient methods for allocating computational resources or reducing cost and latency while maintaining the reliability of services. Most existing scheduling algorithms encounter difficulties in terms of premature convergence and suboptimal performance in multi-objective scenarios. MWOA addresses this challenge by incorporating Levy flight for enhanced global exploration and a chaotic sine map for local exploitation, thus achieving a delicate balance between convergence speed and solution accuracy. The key QoS factors optimized in this study include the task completion time, execution cost, and resource consumption. Simulation results show that MWOA reduces task completion time by 31% and execution cost by 17% compared to traditional algorithms. In this work, we identify the capabilities of MWOA for future development in the task-scheduling model toward unifying dynamic, computationally intensive cloud, heterogeneous, and real-time IoT environments.