Anomaly Perception and Early Fault Prediction in Cloud Services via Graph-Structured Temporal Representation Learning

This study proposes an anomaly perception and prediction model that integrates graph structure and temporal dependence to address the complexity of anomaly detection and fault prediction in cloud service systems. The model first constructs a service dependency graph to capture the structural topology of the system and then employs a temporal encoding module to model the dynamic evolution of service metrics. In the graph-temporal fusion layer, structural and temporal features are deeply integrated to enhance the model's ability to capture anomaly propagation paths and temporal patterns. Using multidimensional monitoring data as input, the model achieves a global representation of cloud service states through spatial aggregation and temporal dependency modeling, leading to stable performance in anomaly detection and root cause localization. Experimental results show that the proposed model significantly outperforms traditional methods in accuracy, recall, and F1-Score, maintaining robustness and generalization under complex topologies and highly dynamic environments. Further experiments on hyperparameter, environment, and data sensitivity verify the model's adaptability to key factors such as learning rate, communication delay, data missing rate, and anomaly ratio. This method provides an interpretable and structured solution for intelligent cloud operations and offers transferable theoretical and technical support for anomaly detection and risk awareness in large-scale distributed systems.