Mobility-Aware Computation Offloading in Satellite Edge Computing Networks

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites' mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem of M obility-aware C omputation O ffloading (MCO) in the SECN via specifying the effect of LEO satellites' high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we design MCO-A , a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance of MCO-A is evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.