Computing-network Collaborative Dynamic Routing and Scheduling Algorithm Based on Deep Reinforcement Learning

To address the issues of insufficient collaboration among computing resources and poor adaptability to task requirements in computing power networks, the computing power routing problem was modeled as a sequential decision problem. A deep reinforcement learning-based computing-aware routing algorithm was proposed for dynamic routing scheduling of computing network collaboration. The idea of hybrid expert models was drawn on and a differentiated expert network was designed based on an encoder-decoder structure for specialized optimization in three typical scenarios: delay-sensitive, ordinary, and computationally intensive. The routing selection space was constrained through an action masking mechanism to achieve efficient hop-by-hop decision-making and output a path containing the optimal computing node. The simulation experiment results show that compared with other routing scheduling algorithms, the proposed algorithm improves service success rate by about 17%, reduces end-to-end latency, optimizes load balancing between nodes, demonstrates good network topology adaptability, and can effectively meet the differentiated needs of diverse computing tasks.