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

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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%。

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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.

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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.