Fine-Grained Resource Management for Edge Computing Satellite Networks

Abstract

The low earth orbit (LEO) satellite network has been a valuable architecture due to its characteristics of wide coverage and low transmission delay. Utilizing LEO satellites as edge computing nodes to provide real-time services for access terminals will be the indispensable paradigm of integrated space-air-ground network. However, it is not easy to design resource management strategies in edge computing satellite (ECS), considering different accessing planes and resource requirements of terminals. Moreover, a comprehensive analysis of the network topology, relative motion, and available resources is required to establish ECS collaborative networks. To address these problems, the dynamic resource allocation architecture and advanced K-means algorithm (AKA) in ECSs are proposed. Then, the extended graph model and breadth-first-search-based spanning tree (BFST) algorithm are utilized to guide the inter-satellite link (ISL) construction. As a result, the ECS collaborative network is established with fine-grained resource management. Simulation results show that the proposed fine- grained resource management scheme is feasible and effective.