Scheduling energy‐conscious tasks in distributed heterogeneous computing systems. (21st July 2021)

• Record Type: Journal Article
• Authors: Liu, Yifan; Du, Chenglie; Chen, Jinchao; Du, Xiaoyan
• Is Part Of: Concurrency and computation. Volume 34:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-21
• Subjects: energy consumption -- energy‐conscious tasks -- heterogeneous distributed system -- task scheduling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6520
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.