JOURNAL ARTICLE

Energy-Aware Scheduling Algorithm with Duplication on Heterogeneous Computing Systems

Abstract

Efficient application scheduling is critical for achieving high performance in heterogeneous computing (HC) environments. Because of its importance, there are many researches on this problem and various algorithms have been proposed. Duplication-based algorithm is a kind of famous algorithm to solve scheduling problem, which achieve high performance on minimizing the overall completion time(makespan) of applications. However, they do not consider energy consumption. With the growing advocacy for green computing system, energy conservation has been an important issue and gained a particular interest. An existing technique to reduce energy consumption of application is dynamic voltage/frequcny scaling(DVFS), but its efficiency is affected by the overhead of time and energy caused by voltage scaling. In this paper, we propose a new energy-aware scheduling algorithm called Energy Aware Scheduling by Minimizing Duplication(EAMD), which considers the energy consumption as well as the makespan of applications. It adopts a subtle energy-aware method to determine and delete the abundant task copies in the schedules generated by duplication-based algorithms, which is easier to operate than DVFS and produces no extra time and energy consumption. This algorithm can reduce large amount of energy consumption while having the same makespan compared with duplication-based algorithms without energy awareness. Randomly generated DAGs are tested in our experiments. Experimental results show that EAMD can save up to 15.59% energy consumption for the existed duplication-based algorithms. Several factors affecting the performance are analyzed in the paper, too.

Keywords:
Energy consumption Computer science Job shop scheduling Scheduling (production processes) Frequency scaling Distributed computing Energy conservation Overhead (engineering) Dynamic voltage scaling Efficient energy use Dynamic priority scheduling Algorithm Real-time computing Embedded system Mathematical optimization Computer network Engineering Routing (electronic design automation) Mathematics

Metrics

36
Cited By
6.44
FWCI (Field Weighted Citation Impact)
31
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Distributed and Parallel Computing Systems
Physical Sciences →  Computer Science →  Computer Networks and Communications
Cloud Computing and Resource Management
Physical Sciences →  Computer Science →  Information Systems
Parallel Computing and Optimization Techniques
Physical Sciences →  Computer Science →  Hardware and Architecture
© 2026 ScienceGate Book Chapters — All rights reserved.