An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty

doi:10.1016/j.ins.2021.11.027

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty
	Zhang, Zhixia 2; Zhao, Mengkai 1; Wang, Hui 3; Cui, Zhihua 1; Zhang, Wensheng 4
刊名	INFORMATION SCIENCES
	2022
卷号	583 页码:56-72
关键词	Interval optimization Interval many-objective optimization Many-objective evolutionary algorithm Cloud task scheduling
ISSN号	0020-0255
DOI	10.1016/j.ins.2021.11.027
通讯作者	Cui, Zhihua(cuizhihua@gmail.com)
英文摘要	Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling. (c) 2021 Elsevier Inc. All rights reserved.
资助项目	National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61772478] ; National Natural Science Foundation of China[62166027] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Key R&D program of Shanxi Province (High Technology)[201903D121119] ; Natural Science Foundation of Jiangxi Province[20212ACB212004]
WOS关键词	OPTIMIZATION ALGORITHM
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000727727800002
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D program of Shanxi Province (International Cooperation) ; Key R&D program of Shanxi Province (High Technology) ; Natural Science Foundation of Jiangxi Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46791]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Cui, Zhihua
作者单位	1.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China 2.Taiyuan Univ Sci & Technol, Sch Elect Informat Engn, Taiyuan, Peoples R China 3.Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Jiangxi, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhixia,Zhao, Mengkai,Wang, Hui,et al. An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty[J]. INFORMATION SCIENCES,2022,583:56-72.
APA	Zhang, Zhixia,Zhao, Mengkai,Wang, Hui,Cui, Zhihua,&Zhang, Wensheng.(2022).An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty.INFORMATION SCIENCES,583,56-72.
MLA	Zhang, Zhixia,et al."An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty".INFORMATION SCIENCES 583(2022):56-72.