On an Intelligent Collaborative Computation Strategy for Low Earth Orbit Satellite Edge Computing Networks

With the decreasing cost of satellite launch, Low Earth Orbit (LEO) satellite constellations have become an important part to complement the terrestrial communication systems for seamless coverage, limited latency, and high throughput. Meanwhile, the developing hardware computation capacity and Inter-Satellite Links (ISLs) have enabled LEO satellites to collaboratively conduct the onboard data processing, which is significantly important for the on-orbit Earth observation, environmental monitoring, and space exploration. However, the highly dynamic topology and heterogeneous resource distribution of LEO networks lead to traditional terrestrial scheduling mechanisms being ineffective. To address these challenges, this paper proposes a collaborative computation offloading framework based on the Software Defined Networking (SDN) technique for heterogeneous LEO satellite networks. The logically centralized controllers obtain global network states (e.g., satellite computing load and ISL conditions) to enable flexible-adaptive task scheduling. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed to decide the task splitting ratio, task processing satellites, and result aggregation satellite under dynamic network conditions. The simulation results demonstrate that the proposed MADDPG strategy significantly reduces the average task completion latency and improves the task success rate compared to benchmarks.