Challenging the generalization capabilities of Graph Neural Networks for network modeling
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
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.
- CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla - Ponències/Comunicacions de congressos 
- Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos 
- Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.118]
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.606]
- LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Ponències/Comunicacions de congressos 
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