负载均衡：处理异构硬件

This blog post describes Uber’s journey towards utilizing hardware efficiently via better load balancing. The work described here lasted over a year, involved engineers across multiple teams, and delivered significant efficiency savings. The article covers the technical solutions and our discovery process to get to them–in many ways, the journey was harder than the destination.

本博客文章描述了Uber通过更好的负载平衡来有效利用硬件的旅程。这项工作持续了一年多，涉及多个团队的工程师，并实现了显著的效率节约。文章涵盖了技术解决方案和我们的发现过程-在很多方面，这个旅程比目的地更加艰难。

Better Load Balancing: Real-Time Dynamic Subsetting | Uber Blog was a related blog post that predates the work described here. We won’t repeat the background–we recommend skimming through the overview of our service mesh there. We’ll also be reusing the same dictionary. This post focuses on the workloads communicating via the service mesh explained above. This covers the vast majority of our stateless workloads.

更好的负载均衡：实时动态子集 | Uber博客是一篇相关的博客文章，早于这里描述的工作。我们不会重复背景知识，我们建议浏览一下那里关于我们服务网格概述的概述。我们还将重复使用相同的词典。本文重点介绍通过上述服务网格进行通信的工作负载。这涵盖了我们绝大多数无状态工作负载。

In 2020, we started work to improve the overall efficiency of Uber’s multi-tenant platform. In particular, we focused on reducing the capacity required to run our stateless services. In this blog post, we’ll cover how individual teams making rational decisions led to inefficient resource usage, how we analyzed the problem and different approaches, and how, by improving load distribution, we got teams to safely increase CPU utilization and drive down costs. The post focuses on CPU only, since this was our primary constraint.

2020年，我们开始致力于提高Uber多租户平台的整体效率。特别是，我们专注于减少运行无状态服务所需的容量。在这篇博文中，我们将介绍个体团队做出理性决策导致资源使用效率低下的情况，我们如何分析问题和采用不同方法，以及通过改进负载分配，我们如何让团队安全地增加CPU利用率并降低成本。本文仅关注CPU，因为这是我们的主要限制。

First, some context: at Uber, most capacity decisions are decentralized. While our platform teams provide recommended targets and tools like auto-scalers, the ultimate decision to adopt specific targets lies in each of the product teams/organizations. A budgeting process exists to curb unlimited allocations.

首先，一些背景...