Joint Optimization of Computation Offloading and Caching for Satellite Edge Computing Based on GRU-SAC Algorithm

Considering the satellites’ wide coverage and independence from geographical constraints, satellite edge computing (SEC) has demonstrated broad application prospects. In this paper, a joint offloading and caching framework is proposed, addressing issues such as redundant data transmission, heterogeneous resources, and the high-speed movement of satellites in SEC. In the framework, latency and system energy consumption are reduced by dynamically caching reusable data and formulating an appropriate offloading strategy. Considering the complexity of the problem, we propose a GRU-SAC algorithm that trains multiple distinct deep neural networks (DNNs) to output offloading and caching actions. It combines maximum entropy with policy gradient to enhance strategy stability and avoid local optima. Furthermore, the algorithm predicts the future request probabilities of tasks as part of the state space, aiding in strategy training and adapting to dynamically changing environments. Simulation results substantiate the effectiveness of our proposed method in reducing latency and system energy consumption. In the simulation environment comprising five users and two satellites, the latency decreased from nearly 132 to around 129, and the energy consumption decreased from nearly 22 to around 18.