在Airbnb的动态Kubernetes集群扩展
Authors: Evan Sheng, David Morrison
Introduction
简介
An important part of running Airbnb’s infrastructure is ensuring our cloud spending automatically scales with demand, both up and down. Our traffic fluctuates heavily every day, and our cloud footprint should scale dynamically to support this.
运行Airbnb基础设施的一个重要部分是确保我们的云计算支出随着需求的增加和减少而自动扩展。我们的流量每天都有很大的波动，我们的云足迹应该动态地扩展以支持这种情况。
To support this scaling, Airbnb utilizes Kubernetes, an open source container orchestration system. We also utilize OneTouch, a service configuration interface built on top of Kubernetes, and is described in more detail in a previous post.
为了支持这种扩展，Airbnb利用了Kubernetes，一个开源的容器编排系统。我们还利用OneTouch，这是一个建立在Kubernetes之上的服务配置界面，在之前的文章中有更详细的描述。
In this post, we’ll talk about how we dynamically size our clusters using the Kubernetes Cluster Autoscaler, and highlight functionality we’ve contributed to the sig-autoscaling community. These improvements add customizability and flexibility to meet Airbnb’s unique business requirements.
在这篇文章中，我们将谈论我们如何使用Kubernetes Cluster Autoscaler来动态调整集群的大小，并强调我们为sig-autoscaling社区贡献的功能。这些改进增加了可定制性和灵活性，以满足Airbnb的独特业务需求。
Kubernetes Clusters at Airbnb
在Airbnb的Kubernetes集群
Over the past few years, Airbnb has shifted almost all online services from manually orchestrated EC2 instances to Kubernetes. Today, we run thousands of nodes across nearly a hundred clusters to accommodate these workloads. However, this change didn’t happen overnight. During this migration, our underlying Kubernetes cluster setup evolved and became more sophisticated as more workloads and traffic shifted to our new technology stack. This evolution can be split into three stages.
在过去的几年里，Airbnb几乎将所有的在线服务从手动协调的EC2实例转移到Kubernetes。今天，我们在近百个集群中运行数千个节点以适应这些工作负载。然而，这种变化并不是一夜之间发生的。在这次迁移过程中，随着更多的工作负载和流量转移到我们的新技术堆栈，我们的底层Kubernetes集群设置不断发展，变得更加复杂。这种演变可以分为三个阶段。
Stage 1: Homogenous Clusters, Manual Scaling
第1阶段：同质化集群，手动缩放
Stage 2: Multiple Cluster Types, Independently Autoscaled
阶段2：多个集群类型，独立的自动缩放
Stage 3: Heterogeneous Clusters, Autoscaled
第三阶段：异构集群，自动缩放