基于改进差分进化算法的云计算任务调度

Xueliang Fu, Yumeng Hu, Yang Sun
{"title":"基于改进差分进化算法的云计算任务调度","authors":"Xueliang Fu, Yumeng Hu, Yang Sun","doi":"10.1145/3421766.3421785","DOIUrl":null,"url":null,"abstract":"In recent years, the introduction of intelligent optimization algorithm into cloud computing task scheduling to deal with the problem of massive task scheduling is a research hotspot. This paper proposes three improved differential evolution cloud computing task scheduling algorithms, and the application of the improved differential evolution algorithm in cloud computing task scheduling problem is mainly studied. The maximum task completion time is optimized by improving parameters F, CR, and variation strategies. Through two sets of simulation experiments, it is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cloud Computing Task Scheduling Based on Improved Differential Evolution Algorithm\",\"authors\":\"Xueliang Fu, Yumeng Hu, Yang Sun\",\"doi\":\"10.1145/3421766.3421785\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the introduction of intelligent optimization algorithm into cloud computing task scheduling to deal with the problem of massive task scheduling is a research hotspot. This paper proposes three improved differential evolution cloud computing task scheduling algorithms, and the application of the improved differential evolution algorithm in cloud computing task scheduling problem is mainly studied. The maximum task completion time is optimized by improving parameters F, CR, and variation strategies. Through two sets of simulation experiments, it is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.\",\"PeriodicalId\":360184,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3421766.3421785\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3421766.3421785","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

近年来,在云计算任务调度中引入智能优化算法来解决海量任务调度问题是一个研究热点。本文提出了三种改进的差分进化云计算任务调度算法,并重点研究了改进的差分进化算法在云计算任务调度问题中的应用。通过改进参数F、CR和变异策略,优化最大任务完成时间。通过两组仿真实验,证明了三种改进的差分进化云任务调度算法比传统的差分进化算法任务完成时间更短,且任务数量越大,算法的性能优化越明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud Computing Task Scheduling Based on Improved Differential Evolution Algorithm
In recent years, the introduction of intelligent optimization algorithm into cloud computing task scheduling to deal with the problem of massive task scheduling is a research hotspot. This paper proposes three improved differential evolution cloud computing task scheduling algorithms, and the application of the improved differential evolution algorithm in cloud computing task scheduling problem is mainly studied. The maximum task completion time is optimized by improving parameters F, CR, and variation strategies. Through two sets of simulation experiments, it is proved that three improved differential evolutionary cloud task scheduling algorithms have less task completion time than the traditional differential evolution algorithm, and the bigger the number of tasks, the more obvious the performance optimization of the algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信