Computing Resource Pricing for Satellites in the OEC System: A Bilevel Optimization Approach

As an emerging paradigm, orbital edge computing (OEC) has garnered significant attention due to its advantages in global coverage and seamless connectivity. Most existing research adopts a user-centric approach, focusing on enhancing user satisfaction, but overlooks the economic objectives of satellites. To fill this gap, this article investigates the collaborative optimization of computing resource pricing, task offloading, and resource allocation, aiming to maximize satellite profits while ensuring fair resource distribution to meet user computing demands. First, a bilevel optimization problem (BOP) is established, considering the coupling between satellite and user strategies. In the upper level, satellites determine unit prices to maximize total profit, while in the lower level, users decide offloading modes and required computing resource amounts based on given prices. Moreover, by deriving the relationship between user resource demands and offloading modes, the mixed variable optimization in the original BOP’s lower level is transformed into a discrete variable optimization. To address the transformed BOP, we reduce the search space through server pruning and design a nested optimization algorithm, which utilizes particle swarm optimization (PSO) and matching game theory in upper and lower levels, respectively. Lastly, experiments prove our algorithm surpasses state-of-the-art in satellite total profit and user task completion rate.