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

• URL: http://arxiv.org/abs/2511.17566v1
• Date: Fri, 14 Nov 2025 03:20:02 GMT
• Title: Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs
• Abstract summary: Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI)<n>These methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared information and reduce training time.<n>We propose CCLH, a novel root cause analysis framework that orchestrates diagnostic tasks based on cascaded conditional learning.
• Score: 6.06702225930801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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