Dynamic Satellite Edge Computing Offloading Algorithm Based on Distributed Deep Learning

Jiaqi Shuai, Haixia Cui, Yejun He, Mohsen Guizani

Abstract: Satellite communication networks with the characteristics of wide coverage, high-deployment flexibility, and seamless communication services can provide communication services to users who do not communicate with ground networks but directly communicate with satellites. In response to the increasing demand for user services, this article proposes a collaborative computing offloading scheme for satellite edge computing networks with a four-layer architecture. By utilizing collaborative computing between ground users and three layers of satellites (low-orbit satellites, edge, and cloud data centers), the service quality for ground users is improved. Considering the mobility of vehicles and satellite nodes, the frequent changes in link states further complicate the design and implementation of such systems, leading to increased latency and energy consumption. This article proposes to optimize the computation offloading decision while satisfying the constraint of satellite computing capabilities, aiming to improve the success rate of tasks and minimize the overall cost of the system. However, with the increase in the number of ground users and satellites, the formulated problem becomes a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve with general optimization algorithms. To address this issue, this article proposes a distributed deep learning-based dynamic offloading (DDLDO) algorithm based on distributed deep learning. The algorithm utilizes multiple parallel deep neural networks (DNNs) to dynamically learn computation offloading strategies. Simulation results demonstrate that the algorithm outperforms other benchmark algorithms in terms of latency, energy consumption, and successful execution efficiency.