Task Offloading Strategy for Dynamic LEO Satellite and Cloud Networks: A Deep Reinforcement Learning-Based Approach

In this paper, we study the task-offloading problem for Internet of Remote Things (IoRT) devices. In the considered scenario, the Low Earth Orbit (LEO) satellite nearest to the devices collects their generated tasks and makes offloading decisions for each task, including selecting the offloading destination and allocating computational resources. Moreover, we consider the motion of satellites within a constellation and assume that a random fraction of computing resources on each edge server is occupied during each time slot. Our objective is to minimize the total longterm latency for all IoRT devices. To address this problem, we propose a Proximal Policy Optimization (PPO)-based algorithm to learn the near-optimal policy. The simulation results demonstrate that our proposed algorithm reduces latency by an average of 21.49% and 50.79% compared to the two benchmark algorithms, perspectively.