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

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.