A Computation Offloading Strategy in LEO Constellation Edge Cloud Network

Abstract

With the rise of a new generation of low Earth orbit (LEO) satellite constellations and the advancement of the 6G network, satellite–terrestrial integrated Internet of Things (IoT) in the future will achieve global coverage through the integration of LEO constellation, and extend computing to the edge of the network through the deployment of edge computing services in LEO constellation so as to meet the demand of mass connection and low latency data processing. The LEO constellation network of the future will be an edge cloud network combining network and computing. In this paper, we propose a computation offloading strategy for the combined optimization of energy and computational load in a LEO constellation edge cloud network (hereinafter referred to as LEO-ECN). First, we establish the LEO-ECN system model, in which the user task can be offloaded to the satellite through the multi-hop path. Then, a cost model considering energy consumption and load calculation is proposed. Finally, a joint optimization problem to minimize energy consumption and balance the LEO-ECN load is established, which is a convex optimization problem. The simulation result demonstrates that, compared with the benchmark strategy, our proposed strategy has better performance and can improve the computing resource utilization of LEO-ECN.