Abstract: Accurate and real-time traffic forecasting plays an important role in the SDN and is of great significance for network traffic engineer,and network plan.Because of the constraints of network topological structure and the dynamic change of time,that is,spatial and time features,network traffic prediction has been considered as a scientific issue.In order to capture the spatial and temporal dependence simultaneously,the Graph Convolutional Gated Recurrent Unit Network model(GCGRU) is proposed,a neural network-based traffic forecasting method,which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU).Specifically,GCN is used to learn complex topological structures to capture spatial dependence and Gated Recurrent Unit is used to learn dynamic changes of traffic data to capture temporal dependence.In terms of model perfor-mance comparison,GCGRU proposed in this paper is compared with classic methods.The evaluation metrics include MSE,RMSE,MAE.The experimental results show that GCGRU can perform better in traffic prediction.

Key words: Graph convolutional gated recurrent network, SDN, Spatial dependence, Temporal dependence, Traffic prediction