Multimodal Reinforcement Learning Aided Dynamic Service Function Chain Deployment in Satellite-Terrestrial Network

In recent years, Satellite-Terrestrial Networks (STNs) have garnered significant attention for extending network coverage to areas beyond the reach of traditional terrestrial networks. With the rapid expansion of STN applications, integrating Service Function Chaining (SFC) technology has become crucial for delivering differentiated services. However, the dynamic and complex structure of STNs presents significant challenges for SFC deployment. To address these, we propose a multimodal reinforcement learning algorithm that uses separate neural networks to process diverse STN data, enabling more effective SFC deployment decisions. Our approach includes a Graph Transformer for processing network states represented as graphs, capturing the relationships between nodes, links, and resource distributions. Additionally, two MLPs are used to handle QoS requests and global network information. Built on these components, the Proximal Policy Optimization (PPO)-based algorithm demonstrates superior performance over conventional AI methods, effectively learning optimal SFC deployment strategies.