一句话：提出首个融合异常检测与根因定位的端到端框架，利用多源数据和多任务学习提升微服务故障排查效果。
微服务系统的复杂性和动态性给可靠性带来挑战，自动化故障排查至关重要。现有方法存在两个问题：一是通常只考虑调用链（traces），忽略了日志和关键性能指标（KPIs）中体现的异常，无法全面刻画系统；二是将异常检测与根因定位视为两个独立阶段，忽略了它们的紧密关联，且检测不准会严重影响定位效果。为此，本文提出 Eadro，首个面向大规模微服务的端到端故障排查框架，将异常检测与根因定位整合在一起。其核心思想是利用不同数据源上的异常表现形式以及检测与定位之间的紧密联系：Eadro 从 traces、logs 和 KPIs 中建模服务内行为与服务间依赖，并通过多任务学习共享两个阶段的知识。在两个广泛使用的基准微服务系统上的实验表明，Eadro 大幅优于现有最优方法，并验证了融合多源数据的有效性。作者还开源了代码与数据。
The complexity and dynamism of microservices pose significant challenges to system reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization after anomaly detection is crucial for ensuring the reliability of microservice systems. However, two significant issues rest in existing approaches: (1) Microservices generate traces, system logs, and key performance indicators (KPIs), but existing approaches usually consider traces only, failing to understand the system fully as traces cannot depict all anomalies; (2) Troubleshooting microservices generally contains two main phases, i.e., anomaly detection and root cause localization. Existing studies regard these two phases as independent, ignoring their close correlation. Even worse, inaccurate detection results can deeply affect localization effectiveness. To overcome these limitations, we propose Eadro, the first end-to-end framework to integrate anomaly detection and root cause localization based on multi-source data for troubleshooting large-scale microservices. The key insights of Eadro are the anomaly manifestations on different data sources and the close connection between detection and localization. Thus, Eadro models intra-service behaviors and inter-service dependencies from traces, logs, and KPIs, all the while leveraging the shared knowledge of the two phases via multi-task learning. Experiments on two widely-used benchmark microservices demonstrate that Eadro outperforms state-of-the-art approaches by a large margin. The results also show the usefulness of integrating multi-source data. We also release our code and data to facilitate future research.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2302-05092,
title = {Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data},
author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Michael R. Lyu},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00150}
}