Enhancing Service Function Chain Performance in SDN/NFV-Enabled Space-Air-Ground Integrated Vehicular Networks: A Three-Sided Stable Matching Approach

Leveraging network function virtualization and software-defined networking, 6G space-air-ground integrated vehicular networks (SAGIVNs) are envisioned to provide efficient and customized services on a global scale. However, on-demand resource allocation for vehicle users (VUs) remains challenging due to the rigidity of service chains, the diversity of user demands, and the complexity of network environments. Existing studies also suffer from inaccurate scene recognition and inefficient virtual resource scheduling. To address these issues, this paper proposes a scene-recognition-based on-demand resource scheduling strategy. Specifically, we first develop a scene recognition mechanism to identify VU demands by analyzing the impact of scenes. Then, the multi-criteria services of multi-dimensional resources are characterized through a polychromatic-set-based description, which supports a unified and extensible representation of heterogeneous resource attributes. Meanwhile, the on-demand matching degree is introduced to measure the satisfaction level of multi-criteria demands. Next, resources are scheduled in the form of groups to improve the efficiency of virtual resource mapping and deployment. Furthermore, a three-sided matching problem among SAGIVN equipment, VU groups, and VUs is formulated, based on which a SAGIVN-equipment-oriented resource scheduling algorithm and a VU-oriented resource scheduling algorithm are developed to achieve accurate service-demand matching. Simulation results demonstrate the superiority of the proposed schemes over benchmark methods.