Research on Dynamic Monitoring and Fault Recovery Mechanism for Container Orchestration Platforms

In response to the issues of delayed fault detection and weak recovery strategy generalization in container orchestration platforms, this paper proposes a dynamic monitoring and intelligent recovery collaborative mechanism. The mechanism includes an elastic-driven hierarchical monitoring architecture, an anomaly prediction model combining temporal convolution and attention mechanisms, a deep reinforcement learning recovery method enhanced by policy migration, and a multi-objective optimization resource scheduling algorithm. Experimental results show that this method significantly improves detection accuracy and recovery efficiency in various fault scenarios, with a fault detection accuracy of 97%, recovery time reduced to under 10 seconds, and resource utilization increased to 86%. This mechanism effectively enhances the anomaly awareness and adaptive recovery capabilities of the container platform, providing a feasible solution for achieving intelligent and highly available operation and maintenance.