Scalable Intelligent Orchestration for SFC: A GraphSAGE-driven Approach
Efficient Service Function Chaining (SFC) orchestration in large-scale NFV environments is often bottlenecked by the non-linear overhead of traditional Graph Convolutional Networks (GCNs). This paper proposes GraphSAGE-DQN, a scalable algorithm that leverages inductive neighbor sampling to decouple state extraction complexity from network size, achieving linear computational complexity. By integrating GraphSAGE embeddings with a Deep Q-Network (DQN), the model optimizes deployment strategies to maximize request acceptance rates. Simulations show that GraphSAGE-DQN outperforms traditional DQN and SECA algorithms, particularly in high-load scenarios where it improves acceptance rates by $\mathbf{1. 6 4 \%}$ and $\mathbf{1 7. 0 \%}$, respectively. These results confirm the model's efficiency and scalability for dynamic SFC orchestration in large-scale networks.