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.
graph convolution neural networksoftware defined networktraffic predictiongating recursionattention mechanismtime characteristics
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