Hierarchical Dynamic Resource Allocation for Computation Offloading in LEO Satellite Networks

With the rapid development of large low Earth orbit (LEO) satellite constellations, satellite edge computing is an emerging topic to provide computing services for Internet of Things (IoT) users, which are not in the coverage of terrestrial networks. For computation offloading in satellite edge computing, it is still challenging to allocate the network resources on-demand for IoT users to improve service experience while reducing energy consumption, since user tasks may be offloaded between different satellites by inter-satellite links (ISLs). In this article, we study the joint optimization problem of computation offloading and resource allocation in cooperative satellite edge computing. Then, a hierarchical dynamic resource allocation (HDRA) algorithm for computation offloading is proposed by introducing breadth first search (BFS) and greedy to tackle the problem, the aim is to minimize service delay and energy consumption jointly. We conduct the experiments to evaluate the performance of the proposed HDRA algorithm, compared with two baselines of BFS-PSO and Gurobi. Experimental results show that the proposed HDRA algorithm can address the formulated problem effectively in satellite edge computing and obtain the results of computation offloading and resource allocation in a low running time.