As the development direction of future network architectures

Software Defined Networks can efficiently set routing schemes by separating the data plane and the control plane. In the process of optimizing a routing scheme

it is the key to accurately predict the network performance under a given routing scheme. This paper uses graph neural networks to model the relationship between physical links and routing scheme paths

so that the model can predict various end-to-end performance indicators (such as delay and jitter) in the network under a given routing scheme and network traffic. This paper uses OMNeT ++ to generate datasets. The experimental results show that the model proposed in this paper can accurately predict end-to-end performance indicators such as delay and jitter. The average relative error of the estimate does not exceed 4.1%. The experiment also compares the end-to-end performance of the traditional shortest path routing algorithm with the optimal routing scheme based on the prediction model proposed in this paper. Compared to the traditional shortest path routing algorithm

the average delay and average jitter are reduced by 19.8% and 33.52%

and the maximum delay and maximum jitter are reduced by 36.18% and 35.45%.