This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

中文翻译：

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etcd 部署对 Kubernetes、Istio 和应用程序性能的影响

这篇经验文章描述了我们在基于 Kubernetes 的环境中进行实验的经验教训，其中最值得注意的是 Kubernetes 控制平面和部署的应用程序的性能都强烈地且以意想不到的方式依赖于 etcd 数据库的性能。这篇文章包含 (a) 详细描述了使用和不使用 Istio 的网络如何在 Kubernetes 中工作，基于 VXLAN 模式下的 Flannel 容器网络接口 (CNI) 提供程序和 IP 虚拟服务器 (IPVS) 支持的 Kubernetes 服务，(b) 一个全面的关于如何使用闭环工作负载生成器进行负载和性能测试的讨论，以及 (c) 一个开源实验框架，可用于在共享云环境中执行实验并探索结果数据。它还表明，即使经过精心准备，统计分析也可能会揭示此类实验产生的数据具有误导性，并且源自 etcd 的非确定性行为可能会影响整个平台和已部署的应用程序。最后，证明为 etcd 使用高性能后备存储可以通过统计显着性 (P < .05) 余量减少此类非确定性行为的发生。这篇经验文章的含义是，研究部署在 Kubernetes 上的应用程序性能的系统研究人员不能简单地将他们的特定应用程序视为正在测试中。相反，必须考虑底层 Kubernetes 和云平台的特殊性，特别是因为它们的性能会影响 etcd 的性能。