Qiyuan Wu

This study focuses on optimizing cache strategies in backend systems and proposes an adaptive cache eviction mechanism based on deep reinforcement learning. Traditional algorithms often struggle with complex and dynamic access patterns. They lack state awareness and the ability to evolve their policies. To address these limitations, this study models cache management as a Markov Decision Process and introduces Deep Q-Networks (DQN) as the decision-making core. An agent is built to learn the optimal actions under different cache states. The model extracts key features such as cache hit rate, access frequency, and time intervals through state representation. It uses experience replay and a target network to ensure stable training. The method is evaluated in several typical experimental scenarios, including hot and cold data switching, changes in cache capacity, and dynamic request distributions. In all cases, the method shows strong adaptability and stability in metrics such as hit rate, response time, and eviction efficiency. The experimental results show that the method effectively improves cache utilization and reduces system latency. It also demonstrates the ability to continuously learn and adjust policies in complex environments. The study provides a systematic evaluation from three perspectives: method modeling, system implementation, and experimental analysis. It confirms the feasibility and practical value of applying deep reinforcement learning to backend cache management. This work offers technical support for intelligent cache optimization in highly dynamic service environments.

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.