RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimizat...

杨利霞

RouteNet: Leveraging Graph Neural Networks for

Network Modeling and Optimization in SDN

Network modeling is a key enabler to achieve
effificient network operation in future self-driving Software
Defifined 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.
( u) D# i/ e, o( GIn this paper we propose RouteNet, a novel network model based
on Graph Neural Network (GNN) that is able to understand
5 n. {( s3 P& w1 qthe complex relationship between topology, routing, and input
traffific 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
2 J. b0 P& L! ]% b2 `2 R% mtopologies, routing schemes and traffific intensity. In our eval
2 ]4 l, I& B# O& r7 \uation, we show that RouteNet is able to predict accurately
the delay distribution (mean delay and jitter) and loss even in
& s( X" S" i' b% }4 t& D2 vtopologies, routing and traffific 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
effificient routing optimization and network planning.


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