Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning

We address a key challenge in cloud native microservice anomaly detection. Existing methods struggle to characterize cross-service dependency propagation and complex temporal dynamics. We propose a temporal representation framework based on cross-service contrastive learning. The method first constructs multi-scale views from service call relations and historical runtime data. It embeds system-level collaborative behaviors into temporal window representations. This yields structure-enhanced input features. It then applies a dual view contrastive encoding strategy with weak augmentation across different time windows. This strategy pulls normal collaborative patterns closer and separates abnormal deviation patterns in the feature space. It improves sensitivity and robustness. The framework further performs unsupervised anomaly scoring through feature aggregation and service-level prototype construction. This avoids extra labeling costs and reduces dependence on manual thresholds. To evaluate the framework, we conduct systematic validation using metrics, logs, and distributed traces from a real open source microservice benchmark system. We also perform multi-dimensional hyperparameter sensitivity analyses across learning rate, batch size, time window length, and the temperature coefficient of the contrastive loss. These analyses reveal stability and adaptability under different dynamic conditions. In comparison with existing baseline models, the method achieves higher accuracy, stronger recall, and better overall performance. This confirms the advantage of cross-service contrastive strategies for capturing anomaly propagation patterns and structural characteristics. Overall, the proposed unified structure of temporal representations and contrastive learning mechanism enables effective unsupervised anomaly detection in collaborative microservice environments. The framework shows strong discriminative capacity and robustness under complex system operations. It provides a reproducible technical solution for improving intelligent operations in cloud native systems.