RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
This work addresses network modeling for efficient operation in software-defined networks, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of accurately predicting network performance indicators like delay and loss in software-defined networks by proposing RouteNet, a Graph Neural Network model that generalizes across topologies and traffic, achieving a worst-case mean relative error of 15.4%.
Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.