Computation Offloading in Delay-Sensitive Multisatellite Cooperative Edge Computing Systems

Multi-access Edge Computing (MEC) technology has been widely considered as a paradigm for offloading computation-intensive and latency-sensitive tasks from ground devices (GDs). However, the deployment of ground edge servers to provide ubiquitous computing services for GDs may incur extremely high costs, primarily due to the uneven distribution of devices and the complexity of geographical environments. As a promising solution, deploying edge servers on Low Earth Orbit (LEO) satellites can offer superior coverage and reduced latency for processing remote terrestrial computational tasks. Nevertheless, considering the limitations in computing resources and energy supply on LEO satellites, it is crucial to enhance task processing efficiency while taking energy consumption into account. In this paper, we consider a multi-satellite cooperative edge computing network (MSCECN) where time-sensitive tasks can be offloaded to an Access Satellite (AS) and multiple Edge Satellites (ESs) through Inter-Satellite Links (ISLs). We formulate a delay-minimization offloading problem by jointly optimizing satellite selection, computing resource allocation, task scheduling, and transmit power control. To solve this problem effectively, we propose a novel joint iterative algorithm called Multi-Satellite Cooperative Resource Allocation (MSCRA), which integrates the relaxation method, the Lagrangian multiplier framework, and the CVX toolbox. Simulation results demonstrate that the proposed optimization scheme achieves a 35.5% reduction in total delay compared to benchmark algorithms and effectively minimizes offloading latency across various resource constraints.