Resilient task offloading in integrated satellite-terrestrial networks with mobility-induced variability

Low Earth Orbit (LEO) satellites have gained significant attention for their low-latency communication and computing capabilities but face challenges due to high mobility and limited resources. Existing studies integrate edge computing with LEO satellite networks to optimize task offloading; however, they often overlook the impact of frequent topology changes, unstable transmission links, and intermittent satellite visibility, leading to task execution failures and increased latency. To address these issues, this paper proposes a dynamic integrated space-ground computing framework that optimizes task offloading under LEO satellite mobility constraints. We design an adaptive task migration strategy through inter-satellite links when target satellites become inaccessible. To enhance data transmission reliability, we introduce a communication stability constraint based on transmission bit error rate (BER). Additionally, we develop a genetic algorithm (GA)-based task scheduling method that dynamically allocates computing resources while minimizing latency and energy consumption. Our approach jointly considers satellite computing capacity, link stability, and task execution reliability to achieve efficient task offloading. Experimental results demonstrate that the proposed method significantly improves task execution success rates, reduces system overhead, and enhances overall computational efficiency in LEO satellite networks.