Queuing Theory-Based Task Offloading for Groundsatellite Edge-Cloud Computing

With the growing demand for computation, traditional computing methods are increasingly unable to meet the requirements of IoT applications. Satellite edge computing, leveraging the coverage advantages of low Earth orbit (LEO) satellites and the low-latency characteristics of edge computing, offers efficient support for remote and emergency scenarios. This paper constructs a “ground-satellite edge-cloud computing” architecture based on queuing theory and proposes a queuing optimization offloading strategy (QAFO) based on the Artificial Fish Swarm Algorithm (AFSA) to reduce task processing delays. Experimental results demonstrate that the QAFO algorithm effectively reduces average delay and enhances system performance while lowering algorithmic complexity.