Adaptive Task Offloading With Spatiotemporal Load Awareness in Satellite Edge Computing

Satellite edge computing, as an extension of ground edge computing, is a key technology for providing computing services by deploying resources on low earth orbit (LEO) satellites. However, the temporal and spatial differences in population density and economic levels may lead to unbalanced computation workloads on LEO satellites. Considering the mobility and limited resources inherent in LEO satellites, effectively utilizing the LEO satellite network to meet global competitive demands for task offloading becomes challenging. Therefore, in this paper, we propose an adaptive task offloading approach with spatiotemporal load awareness, named ATO-SLA, in satellite edge computing, aiming to optimize users' perceived-delay and energy consumption. Specifically, to avoid LEO satellite overload, we first introduce the spatiotemporal load factor, formally modelling the spatiotemporal load-aware task offloading problem. Then, the Markov decision process is employed to structure the task offloading decision process. Afterwards, we propose a task offloading algorithm based on proximal policy optimization strategy to adaptively solve the problem. Finally, experimental results demonstrate that ATO-SLA achieves a lower average delay and average energy consumption compared with other approaches.