PerfDB: A Data Management System for Fine-Grained Performance Anomaly Detection

Joshua Kimball, Rodrigo Alves Lima, Yasuhiko Kanemasa, C. Pu
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Abstract

In this work, we present our performance data management system, PerfDB, that we use to study fine-grained performance anomalies like Millibottlenecks. We use it to present the first experimental evidence of a phenomenon we call, “Localized Latency Requests.” These are performance bugs that are part of the long-tail of system latency. We also provide a population study of Very Long Response Time (VLRT) requests, a separate performance anomaly belonging to the latency long tail, being inducing by millibottlenecks through queueing effects.
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PerfDB:一个用于细粒度性能异常检测的数据管理系统
在这项工作中,我们介绍了我们的性能数据管理系统PerfDB,我们使用它来研究细粒度的性能异常,如millibottleneck。我们用它来展示我们称之为“局部延迟请求”现象的第一个实验证据。这些是性能缺陷,是系统延迟长尾的一部分。我们还提供了超长响应时间(VLRT)请求的总体研究,这是一种属于延迟长尾的单独性能异常,由排队效应引起的微瓶颈引起。
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