通过因果关系推理来定位微服务中的故障根源

Yuan Meng, Shenglin Zhang, Yongqian Sun, Ruru Zhang, Zhilong Hu, Yiyin Zhang, Chenyang Jia, Zhaogang Wang, Dan Pei
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引用次数: 61

摘要

由于微服务架构的灵活性和清晰的逻辑,越来越多的互联网应用正在应用微服务架构。因此,微服务的稳定性对这些应用程序的服务质量至关重要。准确的故障根源定位可以帮助运营商快速恢复微服务故障并减轻损失。尽管跨微服务故障根源定位已经得到了很好的研究,但如何在微服务中定位故障根源,从而快速缓解微服务的故障,还没有得到研究。在这项工作中,我们提出了一个框架,MicroCause,以准确定位微服务中的根本原因监控指标。MicroCause结合了一种简单而有效的路径条件时间序列(PCTS)算法,该算法能准确捕获时间序列数据的顺序关系,以及一种新颖的时间原因导向随机游走(TCORW)方法,该方法集成了监测数据的因果关系、时间顺序和优先级信息。我们基于从全球顶级在线购物服务收集的86份真实失败单来评估MicroCause。我们的实验表明,MicroCause对微服务内部故障根源定位的前5名准确率(AC@5)为98.7%,大大高于最佳基线方法(33.4%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Localizing Failure Root Causes in a Microservice through Causality Inference
An increasing number of Internet applications are applying microservice architecture due to its flexibility and clear logic. The stability of microservice is thus vitally important for these applications' quality of service. Accurate failure root cause localization can help operators quickly recover microservice failures and mitigate loss. Although cross-microservice failure root cause localization has been well studied, how to localize failure root causes in a microservice so as to quickly mitigate this microservice has not yet been studied. In this work, we propose a framework, MicroCause, to accurately localize the root cause monitoring indicators in a microservice. MicroCause combines a simple yet effective path condition time series (PCTS) algorithm which accurately captures the sequential relationship of time series data, and a novel temporal cause oriented random walk (TCORW) method integrating the causal relationship, temporal order, and priority information of monitoring data. We evaluate MicroCause based on 86 real-world failure tickets collected from a top tier global online shopping service. Our experiments show that the top 5 accuracy (AC@5) of MicroCause for intra-microservice failure root cause localization is 98.7%, which is greatly higher (by 33.4 %) than the best baseline method.
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