Dynamic SFC Deployment for SDN/NFV-Based Satellite-Terrestrial Integrated Vehicular Networks

As vehicular network services evolve, the limitations of relying solely on terrestrial networks for communication become increasingly apparent, particularly in terms of network coverage and reliability. Integrating software defined network (SDN) and network function virtualization (NFV) with satellite networks can significantly enhance their networking capabilities. This integration facilitates the formation of satellite-terrestrial integrated vehicular networks (STIVNs), which provide extensive and reliable communication services. A key component in this context is the service function chain (SFC), a prevalent SDN/NFV network service paradigm. The deployment of virtual network functions (VNFs) within an SFC is crucial for optimizing network performance. In this paper, we introduce a novel three-layer architecture for STIVNs, termed layered virtual service satellite-terrestrial integrated vehicular networks (LVS-STIVNs). To optimize quality of service (QoS) and quality of experience (QoE) from the user perspective, we model the dynamic deployment of SFCs as a Markov decision process (MDP). To further enhance adaptability to the dynamic nature of services in satellite networks, we propose an orchestration method based on deep reinforcement learning (DRL), named rDQN-SFC. This method leverages long-short-term memory (LSTM) to effectively capture sequential states. The results indicate that the QoE performance achieved by the rDQN-SFC algorithm is at least 2.02 times that of other DRL baseline algorithms, and the acceptance rate of dynamic SFC deployment is improved by at least 51.1%, demonstrating a more stable adaptive deployment capability in dynamic environments. Additionally, the algorithm exhibits a high time-efficiency ratio, delivering superior service performance in a shorter duration.