Anomaly detection for microservice system via augmented multimodal data and hybrid graph representations

Accurate anomaly detection is essential for ensuring the reliability of microservice systems. Current approaches typically analyze system anomaly patterns using single-modal data (i.e., traces, metrics, and logs) while neglecting the class imbalance between normal and abnormal samples, which can easily lead to misjudgment. This paper propose AMulSys, a graph-based anomaly detection approach, which adopts a hierarchical architecture to simultaneously model three modal data. First, we employ a unified graph structure to analyze the intricate scheduling relationships of traces and integrates various metrics to represent the system’s resource consumption. Meanwhile, to capture the execution paths between logs in interactive services, we design a log heterogeneous graph modeling method, where the service to which the log event belongs is represented as a vertex attribute, and allows distinguishing the paths of the same and different services. Second, we propose a category prior guided multimodal data augmentation algorithm to alleviate class imbalance. It extends the Mixup to multimodal representations and incorporates category priors to bias the synthetic samples labels to the abnormal category. Furthermore, considering the impact of hard samples under class imbalance, we select and assign weights to hard samples, and perform multifaceted contrastive learning based on intra-/inter-modal and inter-sample to optimize pseudo sample. Evaluation results on two real microservice system datasets show that AMulSys outperforms state-of-the-art approaches and achieves an F1-score higher than 0.97.