一句话：提出 COT，通过挖掘服务关联对大规模生产云故障进行快速根因服务定位。
云服务故障常影响多个服务，定位包含根因的服务是减轻影响的关键。工业实践通常以 bootstrap 方式人工逐服务回溯，面对大量相互依赖的服务时耗时耗力。本文提出 COT，首个从服务关联全局视角进行故障分诊的方法：它从故障诊断数据中挖掘服务间关联，并在历史故障上学习后推断新故障的根因服务。作者在 Microsoft Azure 一年的真实数据上实现并评估 COT，分诊准确率达 82.1%–83.5%，较当时最优方法提升 28.0%–29.7%。
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%.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{Wang0WKZZGSYLXZ21,
title = {Fast Outage Analysis of Large-Scale Production Clouds with Service Correlation Mining},
author = {Yaohui Wang and Guozheng Li and Zijian Wang and Yu Kang and Yangfan Zhou and Hongyu Zhang and Feng Gao and Jeffrey Sun and Li Yang and Pochian Lee and Zhangwei Xu and Pu Zhao and Bo Qiao and Liqun Li and Xu Zhang and Qingwei Lin},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00085}
}