SD-SAC: An intelligent DRL approach for QoS-aware atomic task offloading in hybrid LEO satellite-edge networks

Abstract

The increasing computational demands of Internet of Things (IoT) devices require efficient Mobile Edge Computing (MEC) strategies, especially in remote and underserved areas. Traditional terrestrial MEC solutions often encounter challenges such as computational overload and latency issues. This paper introduces a novel intelligent Deep Reinforcement Learning (DRL)-based approach, named Software-Defined Soft Actor-Critic (SD-SAC), specifically tailored for hybrid Low Earth Orbit (LEO) satellite-edge networks. The proposed framework integrates hierarchical Software-Defined Networking (SDN) with DRL to dynamically optimize atomic task offloading decisions. Unlike conventional methods, our solution categorizes satellites into computational edge nodes and non-computational relay nodes, allowing for optimized resource utilization and enhanced task processing. The SD-SAC algorithm employs real-time network state monitoring and adaptive decision-making to effectively balance computational loads, reduce latency, and maximize the task success rate. Comprehensive simulations validate that the SD-SAC framework significantly outperforms traditional offloading methods by improving Quality of Service (QoS) satisfaction, minimizing delays, and ensuring efficient CPU and bandwidth utilization. This research offers a robust solution for managing computational tasks in dynamic satellite-edge computing environments, demonstrating clear advantages in QoS-aware task atomic offloading strategies.