CAVIAR: Disentangling Root Causes with an ICA-based VAE for Large-Scale Microservice Systems

Microservice architectures in modern software engineering generate vast quantities of heterogeneous metrics, making fault diagnosis notoriously difficult. Conventional root cause analysis (RCA) methods often struggle with high-dimensional, diverse data where only a small subset of metrics may truly drive the observed failures. In this paper, we propose CAVIAR (Causality-based Analysis via VAE and ICA for Anomaly Root-cause), a two-phase framework for interpretable RCA in large-scale microservice systems. First, we train a variational autoencoder (VAE) enhanced with Independent Component Analysis (ICA) principles to learn a low-dimensional, disentangled representation of normal microservice operation. By enforcing independence among latent variables, we discover semantically coherent factors, such as specific service loads or network-level conditions. Second, when a fault occurs, we treat anomalies as external interventions on some latent factor and optimize an interventional matrix to identify the culprit dimension. This factor is then mapped back to the original metrics for actionable diagnostics.