Spatiotemporal Multimodal Interaction for Anomaly Detection in Microservices

With the widespread adoption of microservice architectures, anomaly detection has become a key technique to maintain the stable operation of microservice systems and an important research focus in intelligent operations. However, existing methods often rely on single-modal data or simply concatenate metrics, logs, and traces without fully exploring the relationships between modalities. In particular, they tend to overlook spatiotemporal dependencies, making it difficult to comprehensively capture how anomalies evolve in complex systems, which in turn leads to missed detections and false alarms. To address these limitations, this paper proposes STMID (Spatiotemporal Multimodal Interaction for Microservice Anomaly Detection), a spatiotemporal multimodal interaction method for anomaly detection in microservices. The proposed method introduces a service dependency graph to provide a unified representation of multimodal time-series data in microservice systems. It further uncovers cross-modal correlations through multimodal fusion, and models spatiotemporal dependencies to effectively capture anomalous patterns in both temporal dynamics and service dependencies. Based on spatiotemporal dependency modeling results, we use the reconstruction error of the variational autoencoder to measure anomalies, enabling binary classification between normal and abnormal states. Experimental results on two datasets, MSDS and GAIA, show that the proposed method achieves strong overall performance, with an average F1-score of 0.879. It outperforms most baseline methods, with improvements ranging from 3.85% to 42.95%.