RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
This addresses the limitation of queuing theory in network modeling for researchers and operators by providing a more accurate data-driven approach.
The paper tackles the problem of network performance evaluation by introducing RouteNet-Erlang, a graph neural network architecture that models computer networks, and it outperforms a state-of-the-art queuing theory model in all tested scenarios.
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present \emph{RouteNet-Erlang}, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.