一句话：提出 Nezha，通过事件图挖掘多模态可观测数据，在代码区域和资源类型级别实现可解释细粒度根因定位。
大规模微服务系统的根因分析至关重要且困难。现有方法多依赖单模态数据，根因定位的粒度和可解释性受限。Nezha 将 metrics、traces、logs 等异构多模态数据统一表示为事件，并通过构建和挖掘事件图提取事件模式；其核心思想是对比无故障阶段与故障阶段的事件模式，从而以可解释方式定位根因。在代码区域和资源类型级别，Nezha 在两种微服务应用上取得平均 89.77% 的 top1 准确率，显著优于现有最优方法；两项消融实验进一步证实多模态数据融合的贡献。
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 (89.77%) 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.
@inproceedings{YuCLCLZ23,
title = {Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data},
author = {Guangba Yu and Pengfei Chen and Yufeng Li and Hongyang Chen and Xiaoyun Li and Zibin Zheng},
booktitle = {FSE},
year = {2023},
doi = {10.1145/3611643.3616249}
}