Title
A variable neighborhood search algorithm for energy conscious task scheduling in heterogeneous computing systems
Abstract
Energy efficiency in heterogeneous computing systems has attracted increasing interests due to its economic and environmental impacts during recent decades. Based on power-aware hardware techniques, such as dynamic voltage frequency scaling, efforts have been made through task scheduling to reduce the total energy consumption for executing a parallel application while maintaining its time efficiency. In this case, energy conscious task scheduling refers to a bi-objective optimization that aims to minimize the overall completion time (makespan) and the total energy consumption, simultaneously. Existing energy conscious scheduling algorithms conduct energy optimization by means of slack reclamation or a trade-off function. However, the performance of slack reclamation has been proved to be upper-bounded and methods relying on trade-off functions cannot guarantee bi-objective optimization. In this article, an energy conscious task scheduling algorithm is proposed to tackle the above issues based on the framework of variable neighborhood search. Two neighborhood structures are designed to reduce makespan and the total energy consumption, respectively. Furthermore, a pruning technique is incorporated into the algorithm to accelerate the searching process. Extensive experimental results on both randomly generated and real-world applications demonstrate that the proposed algorithm improves the time-efficient schedules on average by 22.4% for the energy consumption and 1.2% for the makespan.
Year
DOI
Venue
2021
10.1002/cpe.6456
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
dynamic voltage frequency scaling (DVFS), energy efficiency, heterogeneous computing system, task scheduling, variable neighborhood search (VNS)
Journal
33
Issue
ISSN
Citations 
24
1532-0626
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yujian Zhang142.45
Chuanyou Li294.31
Fei Tong300.34
Yuwei Xu400.34