URCD: Unsupervised Root Cause Detection in Microservices Architecture with HGAN
Harsh Borse, Utkalika Satapathy, Mainack Mandal, Bivas Mitra · 2024
The shift from monolithic services to microservices brings modularity and elasticity, but detecting faults and anomalies is challenging due to diverse data and evolving technology. The heterogeneous nature of this data complicates the analysis of anomaly signatures across various dimensions. Given the continuous evolution of this technology, exhaustively learning from historical data poses difficulties. To address these challenges, we present URCD, a solution designed to identify and localize faults or anomalies at the application and service level. Remarkably, URCD achieves this without explicit training on faulty data. Our approach integrates heterogeneous microservice data into a bidirectional weighted graph, leveraging a sophisticated Hyper Graph Attention Network (HGAN) model to process heterogeneous data logs generated by microservices. Our evaluation shows the optimal performance of URCD while detecting root cause of anomalies.