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Energy-efficient task scheduling with binary random faults in cloud computing environments
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DOI：10.1016/j.swevo.2025.101877.png)
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Abstract
En 中文
Fault management and energy consumption control have become focal topics in the rapid development cloud computing services. This paper addresses the task scheduling problem with binary random faults in networking and power supply of cloud computing environments and proposes a task scheduling model with multiobjectives of minimizing energy consumption and task completion time while maximizing task completion rate. An estimation of distribution algorithm (EDA) with crowding distance (C) and neighborhood search (N) (EDA-CN) is designed for the model, into which a multi-model probability matrix, regional dislocation backup mechanism, neighborhood search operator, and crowding distance operator are integrated. Numerical experiments examine the effectiveness of EDA-CN in comparison with EDA, EDA-C, and the classic non dominated sorting genetic algorithm III (NSGA3). The results show that EDA-CN consistently outperformed EDA and EDAC, and EDA-CN and NSGA3 performed comparably often yet EDA-CN still outperformed statistically significantly.
Keywords:
Cloud computing environment
Green scheduling
Fault management
Estimation of distribution algorithm
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