A Cluster-Level Scheduling Approach for Heterogeneous Satellite Constellations

With the advancement of satellite resources and service capabilities, efficient inter-satellite scheduling strategies have become a key factor in meeting the growing demand for satellite tasks. The dynamics and limited resources of large-scale satellite networks, along with heterogeneous satellite states and capabilities, significantly complicate service scheduling and task offloading. Effectively and rapidly allocating large-scale satellite resources for inter-satellite scheduling and offloading remains a critical challenge. In this work, we abstract satellite services and formulate a multi-objective optimization problem that jointly considers task allocation and power control, with three optimization objectives: delay, energy consumption, and task load ratio. Leveraging the physical characteristics of large-scale satellite constellations, We divide heterogeneous constellations into multiple clusters and design a two-stage cluster-level scheduling strategy: one for intra-cluster optimization and another for inter-cluster matching. Within each cluster, we design a multi-objective heuristic particle swarm optimization algorithm to quickly generate efficient scheduling strategies. Across clusters, we extend the Kuhn-Munkres algorithm with weighted and virtual nodes to achieve efficient satellite matching for task offloading. Simulation results demonstrate that the proposed cluster-level scheduling algorithms improve scheduling performance compared to other approaches.