TY - GEN
T1 - An efficient differential evolution algorithm for task scheduling in heterogeneous cloud systems
AU - Han, Pengcheng
AU - Du, Chenglie
AU - Liu, Yifan
AU - Chen, Jinchao
AU - Du, Xiaoyan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Due to the ever-growing data and computing requirements of applications, it is very challenging for cloud scheduler to guarantee the optimal solution at a reasonable time. Although varieties of heuristics have been devised to solve the task scheduling problems in heterogeneous cloud systems, the results are still unsatisfactory, especially for large applications. Evolutionary algorithms outperform heuristics in terms of the quality of the solutions, however, they are often time-consuming and need lots of computing power. To address the above problems, this paper proposes an efficient differential evolution algorithm for task scheduling problems. This algorithm extends the canonical differential evolution in three aspects of hybrid initiation population, less greedy mutation and adaptive parameter adjustment. The results of the experiments indicate that our proposed algorithm consistently produces better solutions with smaller makespan and has the advantage of rapid convergence.
AB - Due to the ever-growing data and computing requirements of applications, it is very challenging for cloud scheduler to guarantee the optimal solution at a reasonable time. Although varieties of heuristics have been devised to solve the task scheduling problems in heterogeneous cloud systems, the results are still unsatisfactory, especially for large applications. Evolutionary algorithms outperform heuristics in terms of the quality of the solutions, however, they are often time-consuming and need lots of computing power. To address the above problems, this paper proposes an efficient differential evolution algorithm for task scheduling problems. This algorithm extends the canonical differential evolution in three aspects of hybrid initiation population, less greedy mutation and adaptive parameter adjustment. The results of the experiments indicate that our proposed algorithm consistently produces better solutions with smaller makespan and has the advantage of rapid convergence.
KW - Cloud computing
KW - Differential evolution algorithm
KW - Makespan Optimization
KW - Metaheuristics algorithms
UR - http://www.scopus.com/inward/record.url?scp=85081155677&partnerID=8YFLogxK
U2 - 10.1109/IMCEC46724.2019.8984085
DO - 10.1109/IMCEC46724.2019.8984085
M3 - 会议稿件
AN - SCOPUS:85081155677
T3 - Proceedings of 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019
SP - 1578
EP - 1582
BT - Proceedings of 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019
Y2 - 11 October 2019 through 13 October 2019
ER -