Szczegóły publikacji
Opis bibliograficzny
RouteNet: leveraging Graph Neural Networks for network modeling and optimization in SDN / Krzysztof RUSEK, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio // IEEE Journal on Selected Areas in Communications ; ISSN 0733-8716. — 2020 — vol. 38 no. 10, s. 2260–2270. — Bibliogr. s. 2269–2270, Abstr. — K. Rusek - dod. afiliacja: Barcelona Neural Networking Center, Universitat Politècnica de Catalunya
Autorzy (5)
- AGHRusek Krzysztof
- Suárez-Varela José
- Almasan Paul
- Barlet-Ros Pere
- Cabellos-Aparicio Albert
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130577 |
|---|---|
| Data dodania do BaDAP | 2020-10-09 |
| Tekst źródłowy | URL |
| DOI | 10.1109/JSAC.2020.3000405 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Journal on Selected Areas in Communications |
Abstract
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.
