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Challenging the generalization capabilities of Graph Neural Networks for network modeling

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Suárez-Varela Maciá, José RafaelMés informacióMés informacióMés informació
Carol Bosch, Sergi
Rusek, Krzysztof
Almasan Puscas, Felician PaulMés informacióMés informació
Arias Vicente, MartaMés informacióMés informacióMés informació
Barlet Ros, PereMés informacióMés informacióMés informació
Cabellos Aparicio, AlbertoMés informacióMés informacióMés informació
Document typeConference report
Defense date2019
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectDISEÑANDO UNA INFRAESTRUCTURA DE RED 5G DEFINIDA MEDIANTE CONOCIMIENTO HACIA LA PROXIMA SOCIEDAD DIGITAL (AEI-TEC2017-90034-C2-1-R)
Abstract
Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. Recently a novel Graph Neural Network (GNN) model called RouteNet was proposed as a cost-effective alternative to estimate the per-source/destination pair mean delay and jitter in networks. Thanks to its GNN architecture that operates over graph-structured data, RouteNet revealed an unprecedented ability to learn and model the complex relationships among topology, routing and input traffic in networks. As a result, it was able to make performance predictions with similar accuracy than resource-hungry packet-level simulators even in network scenarios unseen during training. In this demo, we will challenge the generalization capabilities of RouteNet with more complex scenarios, including larger topologies.
CitationSuárez-varela, J. [et al.]. Challenging the generalization capabilities of Graph Neural Networks for network modeling. A: ACM Conference on Special Interest Group on Data Communication. "SIGCOMM’19: proceedings of the 2019 ACM SIGCOMM Conference posters and demos: August 19-23, 2019, Beijing, China". New York: Association for Computing Machinery (ACM), 2019, p. 114-115. 
URIhttp://hdl.handle.net/2117/190287
DOI10.1145/3342280.3342327
ISBN978-1-4503-6886-5
Publisher versionhttps://dl.acm.org/doi/10.1145/3342280.3342327
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  • Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [196]
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  • LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Ponències/Comunicacions de congressos [119]
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