Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds
文献类型：期刊论文
| 作者 | Cai, Xingjuan3,4; Li, Mengxia4; Zhang, Yan4; Zhao, Tianhao4; Zhang, Wensheng2 ; Chen, Jinjun1
|
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 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. |
| WOS关键词 | DATA PLACEMENT STRATEGY ; OPTIMIZATION |
| 资助项目 | Science and Technology Development Foundation of the Central Guiding Local, China[YDZJSX2021A038] ; National Natural Science Foundation of China[61806138] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001088301700001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | Science and Technology Development Foundation of the Central Guiding Local, China ; National Natural Science Foundation of China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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