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 model initializations with strong transferability. This allows the model to adapt rapidly to new service instances and runtime environments under limited observations. It mitigates the impact of anomaly data scarcity and distribution shift. Compared with traditional methods that rely on fixed rules or single-scenario training, the proposed approach emphasizes shared runtime mechanism features. It maintains stable discrimination under noise interference and workload fluctuations. Comparative analysis under a unified data setting shows superior overall accuracy and more consistent anomaly discrimination compared with several representative methods. These results demonstrate strong robustness and generalization. The findings indicate that introducing meta learning into microservice anomaly detection improves adaptability and stability in complex cloud native environments. It provides an effective modeling strategy for anomaly identification in intelligent operations scenarios.