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Multi-modal Observability Data
Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-Modal Observability Data
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.
Guangba Yu
,
Pengfei Chen
,
Yufeng Li
,
Hongyang Chen
,
Xiaoyun Li
,
Zibin Zheng
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