BSDG: Anomaly Detection of Microservice Trace Based on Dual Graph Convolutional Neural Network

Microservice architecture has been widely used by more and more developers in recent years. Accurate anomaly detection is crucial for system maintenance. Trace data can reflect the microservice dependency relationship and response time, which has been adopted for microservice anomaly detection now. However, due to the lack of unification modeling framework of response time and call path, the performance of anomaly detection degrades, and difficult to adapt to downstream tasks. To address the above issues, we propose BSDG, a trace anomaly detection method based on a dual graph convolutional neural network (dualGCN). First, BSDG extracts the microservice call dependencies, combing the learnable node attributes generated by Bi-directional Long Short-Term Memory(BiLSTM) to build an attribute dependency graph combined response time and call path. Then, a self-attention mapping graph is constructed and we use a dualGCN with mutual attention to generate effective feature embedding representation. Finally, BSDG adopts a multilayer perceptron with a new classification loss function to train the model in an end-to-end way for anomaly detection. The experimental results on public benchmarks show that the proposed BDSG outperforms baseline methods. We also conduct experiments on our constructed microservice trace dataset to validate the robustness of BSDG. Experiments show that the BSDG outperforms existing methods in microservice trace anomaly detection.