混合云中微服务工作流的多目标自动伸缩调度

IF 4.4 4区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Enterprise Information Systems Pub Date : 2022-05-03 DOI:10.1080/17517575.2022.2069478

Shi Wang, X. Liu, Ming Gao, Mingxia Chen, K. Yung, Shancheng Jiang

{"title":"混合云中微服务工作流的多目标自动伸缩调度","authors":"Shi Wang, X. Liu, Ming Gao, Mingxia Chen, K. Yung, Shancheng Jiang","doi":"10.1080/17517575.2022.2069478","DOIUrl":null,"url":null,"abstract":"ABSTRACT A novel multi-objective (cost, delay, and reliability) auto-scaling optimisation model is proposed for micro-service workflows in containerised hybrid clouds. We compare the container-based model with VM-based model and conclude that the former significantly supersedes. The benchmark of three mainstream algorithms is conducted by the Hypervolume metric, showed that the performance of MOEA/D is inferior to NSGA family, and NSGA-III is not always superior to NSGA-II. So we design an improved NSGA-II based on dynamically changing crossover and mutation operators, which outperforms NSGA-III both in stability and performance by over 60% and 80% in all multi-scale tests.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multi-objective auto-scaling scheduling for micro-service workflows in hybrid clouds\",\"authors\":\"Shi Wang, X. Liu, Ming Gao, Mingxia Chen, K. Yung, Shancheng Jiang\",\"doi\":\"10.1080/17517575.2022.2069478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT A novel multi-objective (cost, delay, and reliability) auto-scaling optimisation model is proposed for micro-service workflows in containerised hybrid clouds. We compare the container-based model with VM-based model and conclude that the former significantly supersedes. The benchmark of three mainstream algorithms is conducted by the Hypervolume metric, showed that the performance of MOEA/D is inferior to NSGA family, and NSGA-III is not always superior to NSGA-II. So we design an improved NSGA-II based on dynamically changing crossover and mutation operators, which outperforms NSGA-III both in stability and performance by over 60% and 80% in all multi-scale tests.\",\"PeriodicalId\":11750,\"journal\":{\"name\":\"Enterprise Information Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2022-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Enterprise Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/17517575.2022.2069478\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Enterprise Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/17517575.2022.2069478","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 1

摘要

摘要针对容器化混合云中的微服务工作流，提出了一种新的多目标（成本、延迟和可靠性）自动缩放优化模型。我们比较了基于容器的模型和基于VM的模型，并得出结论，前者显著取代了前者。通过Hypervolume度量对三种主流算法进行了基准测试，结果表明，MOEA/D的性能不如NSGA家族，NSGA-III并不总是优于NSGA-II。因此，我们设计了一种基于动态变化的交叉和突变算子的改进NSGA-II，它在所有多尺度测试中的稳定性和性能都优于NSGA-III，分别超过60%和80%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Multi-objective auto-scaling scheduling for micro-service workflows in hybrid clouds

ABSTRACT A novel multi-objective (cost, delay, and reliability) auto-scaling optimisation model is proposed for micro-service workflows in containerised hybrid clouds. We compare the container-based model with VM-based model and conclude that the former significantly supersedes. The benchmark of three mainstream algorithms is conducted by the Hypervolume metric, showed that the performance of MOEA/D is inferior to NSGA family, and NSGA-III is not always superior to NSGA-II. So we design an improved NSGA-II based on dynamically changing crossover and mutation operators, which outperforms NSGA-III both in stability and performance by over 60% and 80% in all multi-scale tests.

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

Enterprise Information Systems 工程技术-计算机：信息系统

CiteScore

11.00

自引率

6.80%

发文量

审稿时长

6 months

期刊介绍： Enterprise Information Systems (EIS) focusses on both the technical and applications aspects of EIS technology, and the complex and cross-disciplinary problems of enterprise integration that arise in integrating extended enterprises in a contemporary global supply chain environment. Techniques developed in mathematical science, computer science, manufacturing engineering, and operations management used in the design or operation of EIS will also be considered.