Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-Modal Observability Data


Root cause analysis (RCA) in large-scale microservice systems is a critical and challenging task. To understand and localize root causes of unexpected faults, modern observability tools collect and preserve multi-modal observability data, including metrics, traces, and logs. Since system faults may manifest as anomalies in different data sources, existing RCA approaches that rely on single-modal data are constrained in the granularity and interpretability of root causes. In this study, we present Nezha, an interpretable and fine-grained RCA approach that pinpoints root causes at the code region and resource type level by incorporative analysis of multi-modal data. Nezha transforms heterogeneous multi-modal data into a homogeneous event representation and extracts event patterns by constructing and mining event graphs. The core idea of Nezha is to compare event patterns in the fault-free phase with those in the fault-suffering phase to localize root causes in an interpretable way. Practical implementation and experimental evaluations on two microservice applications show that Nezha achieves a high top1 accuracy (87.5%) on average at the code region and resource type level and outperforms state-of-the-art approaches by a large margin. Two ablation studies further confirm the contributions of incorporating multi-modal data.

In 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

The blow figure shows the framework of Nezha.

Nazha Framework

Guangba Yu
Guangba Yu
Ph.D. Candidate Focus on Cloud Native

My research interests include cloud computing, microservices, Serverless, AIOps