This repository is a collection of machine learning models for computer networks.
Extended results and code explanation supporting paper Message-Passing Neural Networks Learn Little's Law by Krzysztof Rusek and Piotr Chołda are avalable at in the notebook LittlesLaw. In mpnn we provide a TensorFlow implementation of neural message passing architecture described in the paper.
If you decide to apply the concepts presented or base on the provided code, please do refer our paper: K. Rusek and P. Chołda, "Message-Passing Neural Networks Learn Little’s Law," in IEEE Communications Letters. doi: 10.1109/LCOMM.2018.2886259.
@ARTICLE{8572801,
author={K. {Rusek} and P. {Chołda}},
journal={IEEE Communications Letters},
title={Message-Passing Neural Networks Learn Little’s Law},
year={2019},
volume={23},
number={2},
pages={274-277},
keywords={Delays;Neural networks;Topology;Routing;Network topology;Tools;Machine learning;Knowledge plane;machine learning;message-passing neural networks (MPNN);queuing networks;random graphs},
doi={10.1109/LCOMM.2018.2886259},
ISSN={1089-7798},
month={Feb},}
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
Imlementation is provided in routenet.