Eadro：多源数据微服务端到端故障排除框架
Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data
摘要：
微服务的复杂性和动态性对系统可靠性提出了重大挑战，因此，自动化故障排除至关重要。异常检测后有效的根本原因定位对于确保微服务系统的可靠性至关重要。然而，现有方法存在两个重要问题：（1）微服务生成跟踪，系统日志和关键性能指标（KPI），但现有方法通常只考虑跟踪，无法完全理解系统，因为跟踪无法描述所有异常;（2）微服务故障排除通常包含两个主要阶段，即，异常检测和根本原因定位。现有的研究认为这两个阶段是独立的，忽略了它们之间的密切联系。更糟糕的是，不准确的检测结果会严重影响定位效果。为了克服这些限制，我们提出了Eadro，这是第一个基于多源数据集成异常检测和根本原因定位的端到端框架，用于对大规模微服务进行故障排除。Eadro的关键见解是不同数据源上的异常表现以及检测和定位之间的密切联系。因此，Eadro通过跟踪、日志和KPI对服务内行为和服务间依赖关系进行建模，同时通过多任务学习利用两个阶段的共享知识。在两个广泛使用的基准微服务上的实验表明，Eadro的性能远远优于最先进的方法。结果还表明了多源数据集成的有效性。我们还发布了我们的代码和数据，以方便未来的研究。
英文摘要：
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.
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。
