DGERCL: A Dynamic Graph Embedding Approach for Root Cause Localization in Microservice Systems

Root cause localization in microservice systems refers to finding the root cause that causes system anomalies using system information. Many methods construct a graph structure and perform random walk on it to localize the root cause. This is not suitable for larger systems due to the high computational overhead. Besides, the constructed graph is usually static which mismatches with evolving metrics. Different metrics also contribute differently to determining root cause. To address these challenges, we have developed DGERCL, a novel method that employs dynamic graph embedding to localize root causes in microservice systems. We construct a dynamic graph where nodes, edges, and features correspond to microservices, invocations, and metrics. DGERCL first gets invocation information by aggregating node embedding and features via a trainable structure. An LSTM then processes invocation information to update node embedding. We also propose a neighbor information aggregation method to enrich structure information and a self-attention-inspired mechanism to leverage the importance of metrics for better mining metrics information. Finally, a classifier maps node embedding learned by LSTM to possibilities belonging to root cause. We conduct comprehensive experiments on two microservice benchmarks. Our model achieves good results which demonstrates the effectiveness of DGERCL.