Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs

TL;DR

This paper introduces CCLH, a novel framework for root cause analysis in microservice systems that models causal dependencies and group influences using cascaded conditional learning and hypergraphs, improving diagnostic accuracy.

Contribution

The paper proposes a new cascaded conditional learning framework with hypergraph modeling to better capture causal and group influences in root cause analysis for microservices.

Findings

CCLH outperforms existing methods in RCL and FTI tasks.

Hypergraph modeling effectively captures group influences.

Cascaded conditional learning improves inter-task collaboration.

Abstract

Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared information and reduce training time. However, this simplistic integration neglects the causal dependencies between tasks, thereby impeding inter-task collaboration and information transfer. Second, these existing methods primarily focus on point-to-point relationships between instances, overlooking the group nature of inter-instance influences induced by deployment configurations and load balancing. To overcome these limitations, we propose CCLH, a novel root cause analysis framework that orchestrates diagnostic tasks based on cascaded conditional learning. CCLH provides a three-level taxonomy for group influences between instances and incorporates a heterogeneous hypergraph to model these relationships, facilitating the simulation of failure propagation. Extensive experiments conducted on datasets from three microservice benchmarks demonstrate that CCLH outperforms state-of-the-art methods in both RCL and FTI.