Chain-of-Event: Interpretable Root Cause Analysis for Microservices through Automatically Learning Weighted Event Causal Graph

Abstract

This paper presents Chain-of-Event (CoE), an interpretable model for root cause analysis in microservice systems that analyzes causal relationships of events transformed from multi-modal observation data. CoE distinguishes itself by its interpretable parameter design that aligns with the operation experience of Site Reliability Engineers (SREs), thereby facilitating the integration of their expertise directly into the analysis process. Furthermore, CoE automatically learns event-causal graphs from history incidents and accurately locates root cause events, eliminating the need for manual configuration. Through evaluation on two datasets sourced from an e-commerce system involving over 5,000 services, CoE achieves top-tier performance, with 79.30% top-1 and 98.8% top-3 accuracy on the Service dataset and 85.3% top-1 and 96.6% top-3 accuracy on the Business dataset. An ablation study further explores the significance of each component within the CoE model, offering insights into their individual contributions to the model’s overall effectiveness. Additionally, through real-world case analysis, this paper demonstrates how CoE enhances interpretability and improves incident comprehension for SREs. Our codes are available at https://github.com/NetManAIOps/Chain-of-Event.