RouteNet is a neural architecture for network performance evaluation first proposed in the paper
Unveiling the potential of GNN for network modeling and optimization in SDN by K. Rusek, J. Suárez-Varela, A. Mestres, P. Barlet-Ros, A. Cabellos-Aparicio accepted for ACM Symposium on SDN Research, April 2019, San Jose, CA, USA. arXiv:1901.08113.
An extended version of the model is presented in the paper RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio arXiv:1910.01508.
If you decide to apply the concepts presented or base on the provided code, please do refer our paper.
@ARTICLE{9109574,
author={K. {Rusek} and J. {Suárez-Varela} and P. {Almasan} and P. {Barlet-Ros} and A. {Cabellos-Aparicio}},
journal={IEEE Journal on Selected Areas in Communications},
title={RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN},
year={2020},
volume={38},
number={10},
pages={2260-2270},
doi={10.1109/JSAC.2020.3000405}}
Datasets used for training are available at KDN website
For training simulation, data must be converted to TFrecords using upcdataset.py script.