Welcome to Queuinx’s documentation!
Queuinx is an implementation of some queuing theory results in JAX that is differentiable and accelerator friendly. The particular focus is on networks of finite queues solved by fixed point algorithm of a RouteNetStep step. The API is designed to follow jraph <https://github.com/google-deepmind/jraph> and can be considered as a reference implementation of RouteNet neural architecture.
The use of JAX a machine learning framework as the basis for the implementation allows the use of advanced computational tool like differentiable programming, compilation or support for accelerator.
Instalation
You can install Queuinx from pypi
pip install queuinx
or the latest version form github
pip install git+https://github.com/krzysztofrusek/queuinx.git
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}
}