Edge-Cloud Collaborative Caching Strategy Optimization in Satellite CDN Based on Deep Reinforcement Learning

With the rapid development of new era satellite communication technology, content delivery network (CDN) using satellite is a paradigm to build intelligent distribution networks. However, how to improve the performance of the edge and cloud resources is still a challenge when satellites perform CDN distribution. Considering that the problem is a non-convex mixed-integer optimization problem, we propose a greedy strategy-deep deterministic policy gradient (GS-DDPG) algorithm by combining deep reinforcement learning with traditional optimization algorithms. A trial-reward feedback mechanism is established to accumulate experience and learn the caching policy of the network. The algorithm aims to minimize delay and transmission energy consumption, which optimizes the edge-cloud collaborative caching decision and satellite resource scheduling problem in the satellite CDN distribution task. Simulation results show that the proposed GS-DDPG algorithm can effectively improve the system efficiency in different network environments.