Latency-Efficient Server Placement for LEO Satellite Edge Computing

As the demand for computing in remote areas increases, Low Earth Orbit (LEO) satellites are transitioning from merely serving as communication roles to becoming providers of computing services. LEO satellite edge computing is being integrated into satellite networks, by placing servers on LEO satellites for task processing, to enable in-orbit computing. A key issue is how to optimally place servers in a constellation to support in-orbit computing of tasks effectively. This work proposes a LEO satellite server placement method that minimizes the average response latency of tasks processed on servers while reducing constellation construction costs by strategically selecting a subset of satellites in a constellation for server placement. First, we utilize real-world datasets from remote area devices and remote sensing satellites to derive the task distribution. To enable a unified representation of in-orbit computing tasks (ICT), we develop a unified model that standardizes task data from both sources. Second, we formulate the server placement process as an optimization problem that incorporates both spatial and temporal characteristics of the satellite network, aimed at minimizing the average response latency of ICT processing. To solve this problem, we design an Improved Genetic Algorithm with Simulated Annealing (IGASA) to search for the effective server placement. Simulation results demonstrate that our method achieves superior performance in minimizing the average response latency of task processing compared to baseline server placement methods and converges rapidly.