No such thing as a “free launch”? Systematic benchmarking of containers

Tianming Wei, Madhav Malhotra, Bing Gao, Tomas Bednar, Derek Jacoby, Y. Coady
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引用次数: 4

Abstract

Docker containers have recently become an extremely popular means of building robust, modular systems. Essentially, many architectures leverage lightweight virtualization to manage micro-services. Surprisingly however, there are very few studies revealing the overheads, such as starting new containers in orchestration systems, such as Kubernetes. Though traditional Virtual Machines (VMs) can take on the order of minutes to launch, containers are much faster and the launch times can be on the order of seconds. These overheads typically considered to be negligible compared with the benefits of container-based systems, however, are the predictable? Our work investigates these costs in a systematic study within a private cloud platform. The evaluation outlines a process for studies of this kind. Our results confirm that launch times of VMs are in the range of minutes, whereas containers typically only take seconds. However, these results also show that launch times for new containers do not always scale linearly. Specifically, we identify our system organized by Minikube, a tool that eases local deployment of Kubernetes, introduces a penalty on launch times once the number of containers exceeds 80% of the maximum number of pods available for the cluster. This work demonstrates the presence of unexpected overheads and the need for our proposed systematic infrastructure for testing deployments of containerized services at scale.
没有所谓的“免费发行”?对容器进行系统基准测试
Docker容器最近已经成为构建健壮的模块化系统的一种非常流行的方式。从本质上讲,许多体系结构利用轻量级虚拟化来管理微服务。然而,令人惊讶的是,很少有研究揭示了开销,例如在编排系统(如Kubernetes)中启动新容器。虽然传统的虚拟机(vm)可能需要几分钟才能启动,但容器要快得多,启动时间可能只有几秒钟。与基于容器的系统的好处相比,这些开销通常被认为是微不足道的,然而,这些开销是可预测的吗?我们通过对私有云平台的系统研究来调查这些成本。评估概述了这类研究的过程。我们的结果证实,虚拟机的启动时间在几分钟内,而容器通常只需要几秒钟。然而,这些结果也表明,新容器的启动时间并不总是线性扩展的。具体来说,我们确定我们的系统是由Minikube组织的,Minikube是一个简化Kubernetes本地部署的工具,一旦容器数量超过集群可用的最大pod数量的80%,就会对启动时间进行惩罚。这项工作证明了意外开销的存在,以及我们提出的用于大规模测试容器化服务部署的系统基础设施的需求。
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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