发表机构
Tel Aviv University（特拉维夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据中心服务尾部延迟优化，提出PonyTail性能分析工具，识别服务时间异常根因，优化后第99百分位服务时间和延迟分别提升10.7%–46%和21%–5倍。
AI 中文摘要
数据中心服务必须满足严格的尾部延迟服务水平目标。关于改进尾部延迟的研究主要集中于通过近似最优的请求调度策略来减少排队延迟。随着请求调度接近最优，进一步改进尾部延迟的主要途径是减少请求的服务时间。不幸的是，数据中心服务缺乏明显的热点来进行一般的服务时间优化。我们指出了一个新的优化机会，即针对具有分散服务时间的服务的尾部服务时间进行优化，这种服务在数据中心中很常见。为了展示这种方法的可行性和收益，我们提出了PonyTail，一种性能分析方法和工具包，用于分析与尾部服务时间相关的服务级异常行为。PonyTail帮助性能工程师识别导致服务时间异常的控制流和微架构模式及其根本原因。我们将PonyTail应用于分析五个延迟关键型服务，包括一个内存数据库系统和一个在线搜索引擎。基于PonyTail的分析，我们优化了其中一些服务，将其第99百分位服务时间和延迟分别提高了10.7%–46%和21%–5倍。
英文摘要
Datacenter services must meet tight tail latency service-level objectives. Research on improving tail latency focuses on reducing queuing delay by approximating optimal request scheduling policies. As request scheduling nears optimality, the primary way to further improve tail latency is to reduce the service time of requests. Unfortunately, datacenter services lack obvious hotspots for general service time optimization. We point out a new optimization opportunity in targeting the tail service time of services with dispersive service times, which are common in datacenters. To show the feasibility and benefit of this approach, we present PonyTail, a performance analysis methodology and toolkit for analyzing service-level outlier behaviors associated with tail service time. PonyTail helps performance engineers identify control-flow and microarchitectural patterns responsible for service time outliers, as well as their root causes. We apply PonyTail to analyze five latency-critical services, including an in-memory database system and an online search engine. Based on PonyTail's analysis, we optimize some of the services, improving their 99th percentile service time and latency by 10.7%--46% and 21%--$5\times$, respectively.
