Abstract:

To address the challenges of limited satellite-ground bandwidth and heterogeneous Quality of Service （QoS） requirements for tasks in Low Earth Orbit （LEO） satellite networks， an intelligent computing-aware routing method based on task priority is proposed. First， considering satellite computing and storage resources， a resource-constrained LEO satellite network model is constructed. The sun outage phenomenon is incorporated to dynamically evaluate link availability， thereby capturing the impact of space environmental disturbances on routing decisions. Second， a QoS classification and priority scheduling mechanism is established to characterize the heterogeneous requirements of tasks in terms of latency and packet loss rate. Subsequently， a joint optimization model for computation offloading and data transmission is formulated with the objective of minimizing the end-to-end average delay. Furthermore， the optimization problem is transformed into a Markov Decision Process to enable reinforcement learning-based solutions. A task-priority queue is introduced in task processing and data forwarding， and QoS constraints are incorporated to dynamically filter candidate action space. Meanwhile， task-priority weights are embedded into the reward function. Based on these designs， a Dueling Double Deep Q-Network （Dueling-DDQN）-based computing-aware routing decision algorithm is designed to achieve adaptive optimization of computation offloading and routing decisions. Simulation results demonstrate that， under a constellation of 288 satellites， the proposed method outperforms the Proximal Policy Optimization algorithm under different traffic loads. In particular， the average end-to-end delay is reduced by 4.28%–14.53%， and the task computing ratio is increased by 1.44%–2.89%. Moreover， the proposed method maintains good applicability across different constellation scales.

Key words: low earth orbit satellite, task priority, dueling-DDQN, computing-aware routing, delay optimization