Multi-source Anomaly Detection For Microservice Systems
Zhengxin Li, Junfeng Zhao, Jia Kang · 2024
Microservices architecture has advantages such as independent development, independent deployment, scalability, and reusability. However, faults are inevitable during the operation of microservices systems. This paper introduces an anomaly detection approach based on multi-source data, which combines multi-level attention mechanisms and multi-scale convolutional neural networks. It designs feature extraction modules for different data sources, effectively capturing features of log data and KPI (Key Performance Indicator) data. The extracted features from different data sources are input in parallel to an attention network, where they are weighted and fused. Finally, the fused features are input into the anomaly detection model for detection. We deployed an open-source benchmark microservices system, TrainTicket [1], and injected various typical faults to validate our approach. Experimental results indicate that compared to existing approach, this approach can more accurately identify anomalies.