A Simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications

L. Baresi, G. Quattrocchi
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引用次数: 2

Abstract

Dynamic resource allocation is the mechanism that allows one to change the resources associated with applications at runtime and match their actual needs. The autoscaling solutions offered by cloud infrastructures are probably the most widely-used incarnation of this concepts. Originally conceived to manage virtual machines according to user-defined rules, they are now much more sophisticated and can also allocate containers (lighter than virtual machines). This paper surveys the autoscaling solutions provided by the major cloud vendors and analyzes the services they provide. It also compares them against the solution we developed, called COCOS autoscaling. We simulated the different proposals and fed them with diverse workloads. Obtained results show that COCOS autoscaling outperforms its competitors in most of the cases: it optimizes resource allocation and keeps applications' response times under set thresholds.
基于仿真的工业自动缩放解决方案和云应用COCOS的比较
动态资源分配是一种允许在运行时更改与应用程序相关的资源并匹配其实际需求的机制。云基础设施提供的自动伸缩解决方案可能是这一概念最广泛使用的体现。最初的设想是根据用户定义的规则来管理虚拟机,现在它们要复杂得多,还可以分配容器(比虚拟机轻)。本文调查了主要云供应商提供的自动扩展解决方案,并分析了他们提供的服务。它还将它们与我们开发的解决方案COCOS自动缩放进行了比较。我们模拟了不同的提案,并为它们提供了不同的工作负载。获得的结果表明,COCOS自动伸缩在大多数情况下优于其竞争对手:它优化了资源分配,并将应用程序的响应时间保持在设定的阈值以下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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