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

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. 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. T o 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 mi-croservice benchmarks demonstrate that CCLH outperforms state-of-the-art methods in both RCL and FTI. Microservice architecture has been widely adopted by cloud-native enterprises due to its flexibility, scalability, and loose coupling. In microservice systems (MSS), each microser-vice typically reproduces multiple instances, which collaborate with instances affiliated with other microservices to handle user requests [1], [2]. As these systems scale up, they may suffer from reliability issues, aka failures, attributable to the increasing complexity and dynamicity. Worse still, diagnosing failures in microservice systems is labor-intensive and time-consuming, due to the intricate failure propagation and the overwhelming volume of telemetry data. For example, GitHub once took approximately one and a half hours to resolve a failure that disrupted the codespace service, affecting millions of developers and repositories [3]. Traditional root cause analysis (RCA) in MSS encompasses two tasks: root cause localization (RCL) and failure type identification (FTI).