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标题: RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimizat... [打印本页]
作者: 杨利霞    时间: 2020-11-16 15:20
标题: RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimizat...
RouteNet: Leveraging Graph Neural Networks for

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Network Modeling and Optimization in SDN
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Network modeling is a key enabler to achieve( I- V7 f1 c3 m. ~; D. b
effificient network operation in future self-driving Software
$ M9 Z" Z6 q$ N  X8 DDefifined Networks. However, we still lack functional network
* v6 S* P" ]& }( i; X( W) ~models able to produce accurate predictions of Key Performance
1 h/ P4 a. B- p, }9 o3 S9 SIndicators (KPI) such as delay, jitter or loss at limited cost.
* r! U& _0 V! a1 ?% Y+ @& A" `In this paper we propose RouteNet, a novel network model based
# r; I$ N9 ^/ l6 _on Graph Neural Network (GNN) that is able to understand
" l7 f+ e: e& G) w' N# a" fthe complex relationship between topology, routing, and input& d  e& c: E- _7 F  [! {( W
traffific to produce accurate estimates of the per-source/destination9 I1 L; P9 y/ C) x; P  q
per-packet delay distribution and loss. RouteNet leverages the
0 [4 z( F5 D! V* v5 vability of GNNs to learn and model graph-structured information
1 x$ A* f+ p: r5 y+ {and as a result, our model is able to generalize over arbitrary0 s: y% I/ k+ t% \- I+ D6 E- Z
topologies, routing schemes and traffific intensity. In our eval# j. U$ T- z2 d  O$ x$ W- p& l2 \: _
uation, we show that RouteNet is able to predict accurately# S- R# ]* v7 e7 J
the delay distribution (mean delay and jitter) and loss even in
  M  z; c4 k' Y! L  L" J5 Ltopologies, routing and traffific unseen in the training (worst case
6 h  m* u# Z9 P7 x; `MRE = 15.4%). Also, we present several use cases where we, ?& G; T% [/ l
leverage the KPI predictions of our GNN model to achieve% j% O9 V5 g3 d
effificient routing optimization and network planning.
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