Adaptive AI Spatiotemporal Modeling with Dependency Drift Awareness for Anomaly Detection in Large-Scale Clusters

This study proposes a spatiotemporal framework with cross-node dependency modeling to address anomaly identification in distributed systems under highly dynamic workloads, multi-node coupling, and complex topology changes. The framework first builds a multi-scale temporal representation module that extracts local trends, short-term disturbances, and long-term correlations from monitoring sequences to enhance sensitivity to system dynamics. It then introduces a dynamic structure learning mechanism that generates time-dependent structural graphs based on call chains, resource contention, and link interactions, allowing the model to capture dependency drift caused by topology changes during system operation. On this basis, the framework designs a structure propagation and joint embedding module that fuses temporally enhanced features with structural dependency representations into a unified system state vector, enabling the model to understand anomaly propagation paths and cross-node interactions from a global perspective. A latent-space anomaly measurement function is used to identify possible bottleneck anomalies and improve detection performance for complex system behaviors. To validate the method, extensive experiments are conducted on large-scale open cluster monitoring data, evaluating model performance under hyperparameter sensitivity, environment sensitivity, and data sensitivity settings. The results show that the proposed framework maintains stable performance under structural noise, topology perturbations, and workload fluctuations, and achieves higher accuracy, recall, and F1 scores than multiple representative baselines, demonstrating the advantages of spatiotemporal joint modeling for anomaly detection in distributed systems.