Microservice Fault Detection Method Based on BiLSTM-VAE with Multidimensional Feature Fusion

Abstract The complex dynamic dependency and elastic expansion and contraction mechanisms of microservice architectures lead to system faults with hidden propagation paths and diverse anomaly patterns. Traditional fault detection methods have limitations in terms of detection granularity, dependency on supervised learning, and utilization of multi-source data. This paper proposes a multidimensional feature fusion fault detection method based on BiLSTM-VAE. The multidimensional feature call chain (MFT) is constructed by a coordinate format matrix, fusing multi-source data such as instance-level call path structure, response time and resource utilisation; the call chain pattern is classified by edit-distance K-means clustering, combining with BiLSTM-VAE model to capture bidirectional spatial-temporal features of the call chain, and introducing RealNVP a posteriori flow to optimise the modeling of the hidden variable distribution; based on the kernel density estimation The baseline distribution of normal call chain is established, and p-value hypothesis testing is utilised to realize real-time fault determination. Experiments demonstrate that the method achieves F1 values of 0.979 and 0.985 on the 2020AIOps and TT datasets, respectively, which are 1.3% and 0.9% higher than the optimal baseline. The ablation experiments validate the effectiveness of multidimensional feature fusion, pattern clustering, and a posteriori flow modules. The method breaks through supervisory dependence through unsupervised learning, significantly improving the accuracy and robustness of instance-level fault detection.