Joint Optimization of VNF Deployment and Request Scheduling in Mobile Satellite Networks

With the widespread deployment of low earth orbit (LEO) satellite networks, their high dynamism and large-scale introduce new challenges for the management and control of network communication resources and service orchestration. To tackle these challenges, this paper leverages software defined networking (SDN) and Network Function Virtualization (NFV) to the joint optimization of virtualized network function (VNF) deployment and request scheduling, referred to as the Joint VNF Deployment and Scheduling problem for Mobile Satellite Networks (JVDS-MSN). We formulate the JVDS-MSN problem as an Integer Linear Programming model with cross-timeslot service continuity constraints, aiming to minimize the end-to-end communication resource consumption. Given the NP-hard nature of the problem, we first propose an exact optimization method that integrates Dantzig-Wolfe decomposition with branch-and-bound techniques (DW-BP) to obtain optimal solutions. Although the proposed DW-BP algorithm yields high-quality solutions, its computational cost limits its applicability to large-scale scenarios. To address this, we propose a hierarchical reinforcement learning algorithm based on Twin Delayed Deep Deterministic Policy Gradient (HRL-TD3). This algorithm decomposes the VNF deployment and request scheduling tasks into high-level and low-level sub-tasks, thereby enabling more efficient optimization of bandwidth resources. Simulation results show that the proposed DW-BP algorithm efficiently computes optimal solutions, serving as a strong performance baseline. In large-scale and heterogeneous satellite network scenarios, the HRL-TD3 algorithm achieves near-optimal performance with significantly reduced computational overhead. Overall, the proposed method offers a promising solution for scalable and efficient service orchestration in mobile satellite networks.