  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.
