Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network

Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we apply a Dueling-Double Deep Q-Network (DDQN) algorithm enhanced with prioritized experience replay to derive a computation offloading strategy, improving the experience replay mechanism within the Dueling-DDQN framework. Next, we utilize the Deep Deterministic Policy Gradient (DDPG) algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks. Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches, effectively reducing task processing latency and thus improving user experience and system efficiency.