HG-PAD: Heterogeneous Graph Structure Learning Aided Performance Anomaly Diagnosis in Microservice Systems

Microservice architecture offers great scalability and flexibility to the development of online services systems. Performance anomalies, which happen frequently due to code bugs or runtime environment misconfiguration, can severely damage the system availability and cause great losses. However, it is challenging to detect performance anomalies and locate their root causes considering the large volume of monitoring data (e.g., metrics and traces) and the complex dynamic interdependence between heterogeneous services. Against these challenges, we propose HG-PAD, an automatic performance anomaly diagnosis (PAD) framework for microservice systems. We build the multi-relation heterogeneous graph to model the intricate dependency between services. We further design a structure learning mechanism combining graph neural network (GNN) and node embedding learning to capture the dynamic and latent dependencies. Based on the optimized dependency graph, we devise a Conditional Variational Auto-Encoder (CVAE) based unsupervised anomaly detection method and a graph attention network (GAT) based root cause localization method for accurate anomaly diagnosis. We use datasets of different scales based on real world applications to verify the effectiveness of HG-PAD, and the experimental results show that HG-PAD achieves better diagnostic performance compared with existing baseline methods.