自动缩放性能测量工具

Anshul Jindal, Vladimir Podolskiy, M. Gerndt
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引用次数: 7

摘要

越来越多的公司将注意力转移到为他们的云应用程序添加更多的虚拟化层,从而增加应用程序开发、部署和管理的灵活性。层数的增加可能会导致自动伸缩期间的额外开销,也会导致协调问题,因为层可能使用相同的资源,但由不同的软件管理。为了捕获这些多层自动缩放性能问题,开发了一个自动缩放性能测量工具(APMT)。此工具评估云自动缩放解决方案的性能及其组合,以适应不同类型的负载模式。在本文中,我们重点介绍了该工具的体系结构及其配置。使用APMT收集的数据说明了使用Kubernetes pod作为第二层虚拟化的主要IaaS提供商的自动伸缩行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autoscaling Performance Measurement Tool
More companies are shifting focus to adding more layers of virtualization for their cloud applications thus increasing the flexibility in development, deployment and management of applications. Increase in the number of layers can result in additional overhead during autoscaling and also in coordination issues while layers may use the same resources while managed by different software. In order to capture these multilayered autoscaling performance issues, an Autoscaling Performance Measurement Tool (APMT) was developed. This tool evaluates the performance of cloud autoscaling solutions and combinations thereof for varying types of load patterns. In the paper, we highlight the architecture of the tool and its configuration. An autoscaling behavior for major IaaS providers with Kubernetes pods as the second layer of virtualization is illustrated using the data collected by APMT.
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