QoS-Aware Multi-Hop Task Offloading in Satellite-Terrestrial Edge Networks

Supporting Mobile Edge Computing (MEC) in satellite-terrestrial networks (STN) provides essential offloading services for devices for the Internet of Things (IoT) devices in remote areas. However, when terrestrial demands for computing resources are high, the MEC servers on visible LEO satellites may suffer from insufficient capacity, while those on more distant LEO satellites remain underutilized. To address this issue, this paper investigates cooperative task offloading across multiple LEO satellites within an MEC-based satellite-terrestrial network. We propose a Quality-of-Service (QoS)-aware offloading decision and resource allocation scheme supported by a Software-Defined Network (SDN) for a satellite-terrestrial network architecture. This architecture integrates the LEO Walker constellation with satellite ground stations (SGS), with the aim of providing edge computing services to IoT devices in remote areas. To meet the task’s QoS requirements, the tasks can be offloaded to either SGS or LEO satellites within the constellation. To address the challenges of a vast state space and complex action space within the system, we introduce the QOS-Aware MUlti-Hop Task Offloading in Satellite-Terrestrial EDge NEtworks (OUTSIDE) algorithm, which combines the global search capabilities of genetic algorithms with the local refinement strengths of the Lagrangian multiplier method to minimize the total task computation latency while satisfying QoS demands. Finally, comparative analysis and simulation experiments were conducted. These demonstrate that the OUTSIDE algorithm outperforms other approaches in terms of efficiency and effectiveness.