A benchmark for graph neural networks for computer network modeling
Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay).This thesis introduces the benchmark for computer network modeling using the RouteNet Graph Neural Network as well as a routing creation algorithm.
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/168587 |
| Acceso en línea: | https://hdl.handle.net/2117/168587 |
| Access Level: | acceso abierto |
| Palabra clave: | Graph theory Machine learning Neural networks (Computer science) Graph Neural Networks RouteNet Knowledge Defined Networking Intel·ligència Artificial Algoritmes de creació d'enrutaments Modelatge de xarxes de computadors. machone Learning Artifical Inteligence Routing Creation Algorithms Computer Network Modeling Grafs, Teoria de Aprenentatge automàtic Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Informàtica |
| Sumario: | Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay).This thesis introduces the benchmark for computer network modeling using the RouteNet Graph Neural Network as well as a routing creation algorithm. |
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