  Network virtualization (NV) is a technology with broad application prospects.
Virtual network embedding (VNE) is the core orientation of VN, which aims to
provide more flexible underlying physical resource allocation for user function
requests. The classical VNE problem is usually solved by heuristic method, but
this method often limits the flexibility of the algorithm and ignores the time
limit. In addition, the partition autonomy of physical domain and the dynamic
characteristics of virtual network request (VNR) also increase the difficulty
of VNE. This paper proposed a new type of VNE algorithm, which applied
reinforcement learning (RL) and graph neural network (GNN) theory to the
algorithm, especially the combination of graph convolutional neural network
(GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness
value, we set up the objective function of the algorithm implementation,
realized an efficient dynamic VNE algorithm, and effectively reduced the degree
of resource fragmentation. Finally, we used comparison algorithms to evaluate
the proposed method. Simulation experiments verified that the dynamic VNE
algorithm based on RL and GCNN has good basic VNE characteristics. By changing
the resource attributes of physical network and virtual network, it can be
proved that the algorithm has good flexibility.
