基于服务关联挖掘的大规模生产云快速停机分析
Fast Outage Analysis of Large-scale Production Clouds with Service Correlation Mining
摘要：
近年来，基于云的服务正在迅速普及。然而，停电，即，严重事件总是影响多个服务，可能会严重影响用户体验并导致严重的经济损失。定位根本原因服务，即，包含中断根本原因的服务是减轻中断影响的关键步骤。在当前的工业实践中，这通常是以自举的方式执行的，并且在很大程度上取决于人工努力：首先确定直接导致中断的服务，然后在诊断过程中从服务到服务手动追溯可疑的根本原因，直到找到真正的根本原因。不幸的是，生产云系统通常包含大量相互依赖的服务。这种手动根本原因分析通常是耗时且劳动密集型的。在这项工作中，我们提出了COT，第一个中断分流的方法，认为服务相关性的全局视图。COT从故障诊断数据中挖掘服务之间的相关性。在从历史中断中学习之后，COT可以准确地推断出新出现的中断的根本原因。我们实现了COT，并在一个真实的数据集上进行了评估，该数据集包含从世界上具有代表性的云计算平台之一Microsoft Azure收集的一年数据。实验结果表明，COT的分诊准确率为82.1%~ 83.5%，比现有的分诊方法提高了28.0%~ 29.7%。
英文摘要：
Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%~83.5%, which outperforms the state-of-the-art triage approach by 28.0%~29.7%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
