Large-scale software-defined network traffic prediction model based on graph convolutional neural network

In order to improve the accuracy of large-scale software-defined network traffic prediction, a large-scale software-defined network traffic prediction model based on Graph Convolution Neural Network (GCN) is studied. Build a traffic prediction model including GCN layer, Gating Recursive Unit (GRU) layer and self-attention mechanism layer, reconstruct and update the spatial and temporal characteristics of network traffic through GCN layer and GRU layer respectively, input the two features into self-attention mechanism layer together, and obtain the network traffic prediction value output after integration and weighted average operation, to achieve large-scale software-defined network traffic prediction. The experimental results show that the model can accurately predict large-scale software-defined network traffic, reduce the communication packet loss rate and communication delay of the applied network, achieve high-quality and time-efficient network data transmission, and ensure intelligent traffic communication of large-scale software-defined network.