Energy-efficient task scheduling with binary random faults in cloud computing environments

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-02-26 DOI:10.1016/j.swevo.2025.101877

Lei Jin , Jie Yuan , Dequn Zhou , Xiuzhi Sang , Shi Chen , Xianyu Yu , Guohui Lin

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引用次数: 0

Abstract

Fault management and energy consumption control have become focal topics in the rapid development of cloud computing services. This paper addresses the task scheduling problem with binary random faults in the networking and power supply of cloud computing environments and proposes a task scheduling model with the 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.

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云计算环境下具有二进制随机故障的节能任务调度

随着云计算服务的快速发展，故障管理和能耗控制已经成为人们关注的焦点。针对云计算环境下网络和供电中存在二进制随机故障的任务调度问题，提出了一种以最小化能耗和任务完成时间、最大化任务完成率为多目标的任务调度模型。针对该模型设计了一种具有拥挤距离(C)和邻域搜索(N)的分布估计算法（EDA- cn），该算法集成了多模型概率矩阵、区域错位备份机制、邻域搜索算子和拥挤距离算子。数值实验验证了EDA- cn与EDA、EDA- c和经典非主导排序遗传算法III （NSGA3）的有效性。结果表明，EDA- cn持续优于EDA和EDAC， EDA- cn和NSGA3的表现相当频繁，但EDA- cn的表现仍然具有统计学意义。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.