Adaptive Parallel SFC Joint Scheduling for Time-Varying Satellite-Terrestrial Integration Networks

The increasing demand for ultra-low-latency services in satellite-terrestrial integrated networks (STINs) necessitates adaptive resource orchestration to meet stringent QoS requirements under dynamic network conditions. While Network Function Virtualization (NFV) and Service Function Chaining (SFC) offer flexibility, existing approaches primarily focus on static scenarios and fail to adapt to time-varying topology and inter-SFC resource competition in STIN. To address these limitations, we propose an adaptive parallel SFC scheduling framework that optimizes real-time SFC offloading and virtual network function aggregation to minimize end-to-end latency. We formulate the problem as a Mixed-Integer Nonlinear Programming model that jointly optimizes computation offloading, node selection, and link mapping under dynamic network constraints. To avoid the local optimal solution, the proposed framework efficiently estimates and mitigates the global costs caused by offloading and transmission link conflicts. Extensive experimental results demonstrate that our approach reduces the average end-to-end latency by 28.37% compared to state-of-the-art methods under high task density.