Uncovering Microservice Faults: A Temporal Graph Approach to Root Cause Analysis

Microservices have become the backbone of large IT enterprises due to their ability to scale, recover from failures, and adapt to dynamic workloads within cloud-native architectures. Ensuring the reliability of such systems requires accurate and efficient Root Cause Analysis (RCA) to identify and address faults promptly, minimizing disruptions and maintaining service quality. However, current RCA techniques often focus on isolated levels, such as metrics or services, and rely heavily on predefined thresholds or statistical methods, limiting their effectiveness in complex and interconnected systems. We propose a novel unsupervised RCA method based on Temporal Graph Networks (TGNs) to address these limitations. Our approach models the temporal and structural relationships within microservices through the integrated examination of multimodal data to accurately identify faults. Evaluated on an open-source dataset, our method shows superior accuracy compared to SOTA approaches with at least 2% improvement in PR@1, PR@2, and PR@5.