Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds

doi:10.1016/j.eswa.2023.121833

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

	Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds
	Cai, Xingjuan 3,4; Li, Mengxia 4; Zhang, Yan 4; Zhao, Tianhao 4; Zhang, Wensheng 2; Chen, Jinjun 1
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2024-03-15
卷号	238 页码:11
关键词	Evolutionary multitasking algorithms Bi-level optimization Data-intensive scientific workflow Data placement Task scheduling
ISSN号	0957-4174
DOI	10.1016/j.eswa.2023.121833
通讯作者	Li, Mengxia(limengxia19971998@163.com)
英文摘要	With the deployment of workflow and other applications, cloud computing is accessible and offers assistance for optimizing workflow execution and enhancing performance. Existing research, however, tends to disregard the influence of dataset migration on workflow execution and focuses more on task execution time. This study suggests a new model for the problem of data-intensive workflow execution. Firstly, according to the structure of the workflow scheduling problem, it is divided into two sub-problems: data placement and task scheduling. The two sub-problems interact with each other and a bi-level optimum model is established. By seeking a better allocation strategy for the dataset placement and then seeking the best task-scheduling solution. Secondly, an improved multitasking bi-level evolutionary algorithm (IM-BLEA) is proposed. When dealing with the lower-level optimization problem (LLOP), offspring are selected by sorting individuals by their performance and overall performance in the population, and this environmental selection enhances the diversity and searchability of the population. Finally, compared with the other multitasking algorithm, IM-BLEA has good performance. Simulation results based on real scientific workflows show that the algorithm improves the values of transfer time and number of selected data centers by 56% and 10% compared to the comparison algorithm.
资助项目	Science and Technology Development Foundation of the Central Guiding Local, China[YDZJSX2021A038] ; National Natural Science Foundation of China[61806138]
WOS关键词	DATA PLACEMENT STRATEGY ; OPTIMIZATION
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:001088301700001
资助机构	Science and Technology Development Foundation of the Central Guiding Local, China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54355]
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	Li, Mengxia
作者单位	1.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Australia 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China 4.Taiyuan Univ Sci & Technol, Shanxi Key Lab Big Data Anal & Parallel Comp, Taiyuan 030024, Shanxi, Peoples R China
推荐引用方式 GB/T 7714	Cai, Xingjuan,Li, Mengxia,Zhang, Yan,et al. Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,238:11.
APA	Cai, Xingjuan,Li, Mengxia,Zhang, Yan,Zhao, Tianhao,Zhang, Wensheng,&Chen, Jinjun.(2024).Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds.EXPERT SYSTEMS WITH APPLICATIONS,238,11.
MLA	Cai, Xingjuan,et al."Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds".EXPERT SYSTEMS WITH APPLICATIONS 238(2024):11.