Improvements to Deep-Learning-based Feasibility Prediction of Switched Ethernet Network Configurations
Tieu Long Mai, Nicolas Navet · 2021
Graph neural network (GNN) is an advanced machine learning model, which has been recently applied to encode Ethernet configurations as graphs and predict their feasibility in terms of meeting deadlines constraints. Ensembles of GNN models have proven to be robust to changes in the topology and traffic patterns with respect to the training set. However, the moderate prediction accuracy of the model, 79.3% at the lowest, hinders the application of GNN to real-world problems.