Generation of a network slicing dataset: The foundations for AI-based B5G resource management

This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing con...

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Detalhes bibliográficos
Autores: Farreras, Miquel, Paillissé Vilanova, Jordi|||0000-0001-7733-9713, Fàbrega, Lluís, Vilà Talleda, Pere
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/451555
Acesso em linha:https://hdl.handle.net/2117/451555
https://dx.doi.org/10.1016/j.dib.2024.110738
Access Level:acceso abierto
Palavra-chave:5G
B5G
Deep Learning
Network simulation
Network slicing
Quality of service
Transport networks
KPI
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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spelling Generation of a network slicing dataset: The foundations for AI-based B5G resource managementFarreras, MiquelPaillissé Vilanova, Jordi|||0000-0001-7733-9713Fàbrega, LluísVilà Talleda, Pere5GB5GDeep LearningNetwork simulationNetwork slicingQuality of serviceTransport networksKPIÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThis paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the Radio Access Network (RAN) infrastructure. Each sample consists of pairs of a network scenario and the associated performance metrics: the network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of scenarios in multiple situations of over and underprovisioning. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.This project has received funding from the Red temática Go2Edge (Ref.: RED2018-102585-T), from the Ajut Pont UdG 2020/23 and Generalitat de Catalunya through Consolidated Research Group 2017-SGR-1318 and 2017-SGR-1552, the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya for the FI-SDUR fellowship funding 2020 FISDU00590 assigned to Miquel Farreras. Jordi Paillisse is funded by European Union-Next Generation EU, Ministry of Universities and Recovery, Transformation and Resilience Plan, through a call from Universitat Politècnica de Catalunya (Grant Ref. 2022UPC-MSC-93871).Peer ReviewedElsevier20242024-08-0120262026-01-23journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/451555https://dx.doi.org/10.1016/j.dib.2024.110738reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4515552026-05-27T15:37:01Z
dc.title.none.fl_str_mv Generation of a network slicing dataset: The foundations for AI-based B5G resource management
title Generation of a network slicing dataset: The foundations for AI-based B5G resource management
spellingShingle Generation of a network slicing dataset: The foundations for AI-based B5G resource management
Farreras, Miquel
5G
B5G
Deep Learning
Network simulation
Network slicing
Quality of service
Transport networks
KPI
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Generation of a network slicing dataset: The foundations for AI-based B5G resource management
title_full Generation of a network slicing dataset: The foundations for AI-based B5G resource management
title_fullStr Generation of a network slicing dataset: The foundations for AI-based B5G resource management
title_full_unstemmed Generation of a network slicing dataset: The foundations for AI-based B5G resource management
title_sort Generation of a network slicing dataset: The foundations for AI-based B5G resource management
dc.creator.none.fl_str_mv Farreras, Miquel
Paillissé Vilanova, Jordi|||0000-0001-7733-9713
Fàbrega, Lluís
Vilà Talleda, Pere
author Farreras, Miquel
author_facet Farreras, Miquel
Paillissé Vilanova, Jordi|||0000-0001-7733-9713
Fàbrega, Lluís
Vilà Talleda, Pere
author_role author
author2 Paillissé Vilanova, Jordi|||0000-0001-7733-9713
Fàbrega, Lluís
Vilà Talleda, Pere
author2_role author
author
author
dc.subject.none.fl_str_mv 5G
B5G
Deep Learning
Network simulation
Network slicing
Quality of service
Transport networks
KPI
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic 5G
B5G
Deep Learning
Network simulation
Network slicing
Quality of service
Transport networks
KPI
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the Radio Access Network (RAN) infrastructure. Each sample consists of pairs of a network scenario and the associated performance metrics: the network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of scenarios in multiple situations of over and underprovisioning. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-01
2026
2026-01-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/451555
https://dx.doi.org/10.1016/j.dib.2024.110738
url https://hdl.handle.net/2117/451555
https://dx.doi.org/10.1016/j.dib.2024.110738
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
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
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
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1869403387549712384
score 15,811543

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