Graph Pointer Network Assisted Deep Reinforcement Learning for Virtualized Network Embedding

Network Function Virtualization (NFV) improves the flexibility and scalability of network services and reduces operating costs. As the core of research on network virtualization, Virtual Network Embedding (VNE) aims to effectively deploy service requests on physical network components and allocate underlying physical resources. However, network services can be complex and diverse, which makes it difficult for existing embedding methods to effectively utilize the graph structure of services, tackle the complexity of dynamic networks, and provide effective embedding solutions. To this end, we propose GPRL, an online VNE method based on graph pointer network and Deep Reinforcement Learning (DRL). By combining the graph neural network and pointer network, we design a novel graph pointer network as the DRL agent. It employs the graph attention network to encode graph feature data and decodes to output the embedding policy via the pointer network architecture. Furthermore, the Proximal Policy Optimization (PPO) algorithm is used to effectively train the designed agent. The effectiveness and superiority of GPRL are verified by simulation experiments, and GPRL is shown to perform better than existing embedding methods.