关键词: 异常检测, 微服务, 时空增强, 多模态

Abstract: Anomaly detection, as a crucial component for ensuring the health and performance of microservice systems, has become a hot topic in the field of intelligent operations and maintenance. However, existing anomaly detection methods face three significant challenges: (1) Insufficient correlation analysis of heterogeneous monitoring data, such as indexes, logs, and call chains, in microservice systems leads to false negatives. (2) Inadequate mining of spatiotemporal features of heterogeneous data results in false positives. (3) Anomaly detection results fail to comprehensively characterize the state of microservice systems, lacking interpretability. To address these issues, a multimodal spatiotemporal enhanced anomaly detection framework (STEAD) is proposed. The framework effectively unifies the representation of heterogeneous multimodal data generated by microservice systems. A cross-modal attention module is introduced to precisely extract correlations between different data modalities. The correlated data are fed into a spatiotemporal enhanced network, which leverages its powerful spatiotemporal recursive encoding capabilities to deeply mine spatiotemporal features in the data. The encoded results are mapped to a latent space through a variational autoencoder to learn the feature distribution, and anomalies are detected by comparing the differences between the decoded reconstruction results and the original inputs. Extensive experimental results demonstrate that STEAD achieves an average improvement of 7.76%~39.43% in the F1-score compared with existing methods. Moreover, compared with multimodal methods, STEAD reduces model training time by approximately 80%.

Key words: anomaly detection, microservice, spatiotemporal enhancement, multimodality