Demand-Oriented Robust Service Allocation with Multi-Phase Task Matching in Satellite Networks

The rapid growth in satellite internet services, exemplified by constellations like Starlink, has underscored the imperative for efficient service allocation in satellite networks. This poses significant challenges due to the dynamic and multidimensional nature of user demands, which vary across time and space, alongside the fluctuating availability of satellite resources. Traditional static allocation methods are inadequate for these dynamic conditions, often resulting in inefficient resource utilization and compromised service quality. To address these challenges, this paper presents Demand-Oriented Robust Service Allocation (DORSA) with a multi-phase task matching approach. Our method begins with a static matching phase to establish an initial user demand-service match, followed by a dynamic matching phase that leverages a sparse time-varying bipartite graph model to enhance computational efficiency. The DORSA algorithm incorporates local backup-aware matching to rapidly adapt to demand fluctuations and satellite movements, ensuring continuous and high-quality service provision. Simulation results indicate that our approach reduces the average response latency by 74.5% compared to the baseline approach, and by 54.2% compared to the state-of-the-art task allocation algorithm under dynamic demand conditions within satellite networks.