DRL-based automation of Time Sensitive Networks (TSN)

This Master Thesis addresses the routing and scheduling assignment problem of Time Sensitive Networks (TSN), a set of standards that IEEE defined to provide low-latency reliable communications over Ethernet networks. The proposed solutions have been based on Deep Reinforcement Learning (DRL), a subs...

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Detalles Bibliográficos
Autor: García Cantón, Sergi
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
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:2117/418076
Acceso en línea:https://hdl.handle.net/2117/418076
Access Level:acceso abierto
Palabra clave:Time Sensitive Networking (TSN)
Synchronous networks
Machine Learning (ML)
Deep Learning (DL)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
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spelling DRL-based automation of Time Sensitive Networks (TSN)García Cantón, SergiTime Sensitive Networking (TSN)Synchronous networksMachine Learning (ML)Deep Learning (DL)Àrees temàtiques de la UPC::Enginyeria de la telecomunicacióThis Master Thesis addresses the routing and scheduling assignment problem of Time Sensitive Networks (TSN), a set of standards that IEEE defined to provide low-latency reliable communications over Ethernet networks. The proposed solutions have been based on Deep Reinforcement Learning (DRL), a subset of Machine Learning that is very powerful in solving complex sequential decision-making problems. This work is part of the 6GSMART-EZ project, which aims to develop the integration of 5G and TSN networks, so one of the proposed solutions complies with this integration scenario. First, some literature research is conducted to identify the problem to solve and be able to propose adequate solutions. Second, a centralised approach of DRL models has been implemented and tested on a simulated isolated private TSN network to support simple deployments that do not require any integration with 5G networks. Third, a distributed approach with an agent at each side of the 5G network has also been implemented. This approach proposed a network topology with two TSN networks integrated with a 5G network by creating two interconnection points.Universitat Politècnica de CatalunyaCervelló Pastor, Cristina20242024-10-2420242024-11-15master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/418076reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4180762026-05-27T15:37:01Z
dc.title.none.fl_str_mv DRL-based automation of Time Sensitive Networks (TSN)
title DRL-based automation of Time Sensitive Networks (TSN)
spellingShingle DRL-based automation of Time Sensitive Networks (TSN)
García Cantón, Sergi
Time Sensitive Networking (TSN)
Synchronous networks
Machine Learning (ML)
Deep Learning (DL)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
title_short DRL-based automation of Time Sensitive Networks (TSN)
title_full DRL-based automation of Time Sensitive Networks (TSN)
title_fullStr DRL-based automation of Time Sensitive Networks (TSN)
title_full_unstemmed DRL-based automation of Time Sensitive Networks (TSN)
title_sort DRL-based automation of Time Sensitive Networks (TSN)
dc.creator.none.fl_str_mv García Cantón, Sergi
author García Cantón, Sergi
author_facet García Cantón, Sergi
author_role author
dc.contributor.none.fl_str_mv Cervelló Pastor, Cristina
dc.subject.none.fl_str_mv Time Sensitive Networking (TSN)
Synchronous networks
Machine Learning (ML)
Deep Learning (DL)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
topic Time Sensitive Networking (TSN)
Synchronous networks
Machine Learning (ML)
Deep Learning (DL)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
description This Master Thesis addresses the routing and scheduling assignment problem of Time Sensitive Networks (TSN), a set of standards that IEEE defined to provide low-latency reliable communications over Ethernet networks. The proposed solutions have been based on Deep Reinforcement Learning (DRL), a subset of Machine Learning that is very powerful in solving complex sequential decision-making problems. This work is part of the 6GSMART-EZ project, which aims to develop the integration of 5G and TSN networks, so one of the proposed solutions complies with this integration scenario. First, some literature research is conducted to identify the problem to solve and be able to propose adequate solutions. Second, a centralised approach of DRL models has been implemented and tested on a simulated isolated private TSN network to support simple deployments that do not require any integration with 5G networks. Third, a distributed approach with an agent at each side of the 5G network has also been implemented. This approach proposed a network topology with two TSN networks integrated with a 5G network by creating two interconnection points.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-10-24
2024
2024-11-15
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/418076
url https://hdl.handle.net/2117/418076
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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