Adaptive Anomaly Detection in Microservice Systems via Meta-Learning

This study addresses the highly dynamic runtime environment of microservice systems, the complex inter-service dependencies, and the hidden nature of anomalous behaviors. It proposes an anomaly detection method that integrates a meta learning mechanism. Based on multi-source monitoring data, the microservice execution process is modeled as a continuously evolving state sequence. A unified representation learning strategy is used to capture system evolution under normal conditions. The degree of state deviation is then adopted as the basis for anomaly discrimination. During modeling, different services or operating scenarios are treated as independent tasks. A meta learning framework is introduced to learn…