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

杨利霞

RouteNet: Leveraging Graph Neural Networks for

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Network Modeling and Optimization in SDN

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0 _9 D% X% j- F# a+ hNetwork modeling is a key enabler to achieve
effificient network operation in future self-driving Software
9 \. \5 N4 I1 Y! e& f) e- G& E" jDefifined Networks. However, we still lack functional network
! G/ V! L  A3 D( B& J9 Ymodels able to produce accurate predictions of Key Performance
Indicators (KPI) such as delay, jitter or loss at limited cost.
% L/ k; T$ Z% p" i7 Y0 V3 gIn this paper we propose RouteNet, a novel network model based
on Graph Neural Network (GNN) that is able to understand
' b: B! Z0 Z+ @( A, @/ ]the complex relationship between topology, routing, and input
* r' z8 K2 w, f$ E$ c) b$ otraffific to produce accurate estimates of the per-source/destination
per-packet delay distribution and loss. RouteNet leverages the
2 z' p2 m1 h! L/ }7 K+ H( A5 b- m% qability of GNNs to learn and model graph-structured information
and as a result, our model is able to generalize over arbitrary
: M& E9 }$ V+ P) F$ H; {8 Wtopologies, routing schemes and traffific intensity. In our eval
uation, we show that RouteNet is able to predict accurately
, ~* M% E, h& H. y9 Xthe delay distribution (mean delay and jitter) and loss even in
topologies, 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
) X- U# U+ a5 P/ E5 |9 {effificient routing optimization and network planning.
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