Multimodal Anomaly Detection for Microservice Systems via Grassmann Manifolds-Based Graph Fusion

Microservice architecture decomposes complex applications into multiple small, independent services, enabling independent deployment and scaling. However, microservice systems introduce complex and dynamic interactions between service instances. When a service instance fails, it can significantly degrade the performance of the entire system, harm the user experience, and cause substantial economic losses. Therefore, effective anomaly detection for service instances is crucial to ensure system reliability. Metrics, logs, and traces provide complementary insights into microservice operations, and recent approaches have attempted to jointly exploit these modalities for anomaly detection. However, the varying data volumes per interval and heterogeneous data types across modalities, combined with the complex and elusive relationships between them, make effective representation and learning challenging. To address these limitations, we propose MGFusion, an unsupervised, end-to-end multimodal anomaly detection method for microservice systems using Grassmann manifolds-based graph fusion. MGFusion employs a robust method to unify metrics, logs, and traces into time series, enabling consistent processing across modalities. It leverages multiple graph structure learning (GSL) techniques to explore multi-view relationships between the modalities. To mitigate the impact of data noise, we further introduce prior knowledge based on the Adamic-Adar Index (AAI) and employ a Grassmann manifolds-based graph fusion method to combine multiple basic graph structures. Finally, anomaly detection is achieved using Diffusion Convolutional Recurrent Neural Network (DCRNN) predictors and anomaly score calculation. Extensive experiments on a public dataset demonstrate that MGFusion effectively fuses multimodal data and captures complex relationships, significantly improving the accuracy and efficiency of anomaly detection. Compared to the state-of-the-art unsupervised multimodal anomaly detection method, AnoFusion, MGFusion achieves an improvement in the F1 score ranging from 11.1% to 15.3%.