基于图神经网络的SDON性能预测模型OA北大核心

Network performance prediction is the key to achieving efficient network management of software defined optical net-works(SDON),but there is an urgent need for a network performance prediction model that can accurately predict key indicators at limited cost.A graph neural network-based SDON performance prediction model is proposed,which combines BiGRU and Self-Attention mechanisms to learn the complex relationships between network topology,routing,and traffic matrices,accurately estimating the packet delay,jitter,and packet loss rate of the source/destination in the network.This model can be applied to net-works that have not been encountered during training.The experimental results show that in different traffic model tests,the pro-posed model has a significant improvement in average absolute percentage error(MAPE)performance compared to the baseline model.