Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario

Several businesses are nowadays becoming more and more aware of the potential that lies beneath Big Data. From social media ‘titans’ and healthcare companies, to the mobile industry that propelled them, 5th Generation mobile network (5G) will be a fundamental factor that will drive our new reality....

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Autor: Márquez, Cristina|||0000-0002-2924-4362
Tipo de recurso: tesis doctoral
Fecha de publicación:2020
País:España
Institución:IMDEA Networks Institute
Repositorio:IMDEA Networks Institute Digital Repository
Idioma:inglés
OAI Identifier:oai:dspace.networks.imdea.org:20.500.12761/900
Acceso en línea:http://hdl.handle.net/20.500.12761/900
Access Level:acceso abierto
Palabra clave:Big Data
Network slicing
Resource Management
Network Efficiency
Mobile Networks
Slice Orchestration
NFV
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spelling Characterization of mobile network services to assess the impact of network slicing in a nationwide scenarioMárquez, Cristina|||0000-0002-2924-4362Big DataNetwork slicingResource ManagementNetwork EfficiencyMobile NetworksSlice OrchestrationNFVSeveral businesses are nowadays becoming more and more aware of the potential that lies beneath Big Data. From social media ‘titans’ and healthcare companies, to the mobile industry that propelled them, 5th Generation mobile network (5G) will be a fundamental factor that will drive our new reality. Current mobile service usage has been explored for data-driven organizational growth in the touristic sector, as it allows forecasting hotel occupancy rates or targeting customers. However, mobile data is continuously increasing, and therefore it is enormously important to analyze such data for networking purposes. Previous Ericsson Mobility Reports claimed that by the end of 2022 the total monthly traffic associated with mobile devices would be 77 exabytes (EB), representing 20% of the total Internet Protocol (IP) traffic around the world. They also declare that 50 EB/month would come from 2nd Generation mobile network (2G), 3rd Generation mobile network (3G), and 4th Generation mobile network (4G) devices. In terms of 5G subscriptions, it is expected to reach up to 2.8 billion subscriptions globally by the end of 2025, accounting for about 30% of total mobile subscriptions. Experts stake out that by the year 2020, 1.7 megabytes of data will be generated every second for every person on the planet, forcing the network to evolve and adapt to challenging new demands. Network providers not only deal with the deployment of the required resources to support this growth, but also the potential newcomers into the business, and the stringent conditions of the distinct services to be provided. One of the features proposed to face this dilemma is using the network slicing technique. It allows to transform and orchestrate a 5G network by creating multiple logical instances (i.e., slices) on top of it, while Big Data would provide the specifications of the services’ traffic dynamics to be served. In this way, operators achieve the best allocation of resources. This thesis contributes to the ongoing Network Slicing research, assessing a nationwide scenario. Our results show mobile traffic similarities and differences across time, space, and frequency domains, whereas we intend for distinct service clusterizations that would enhance the network efficiency in terms of resource management. For instance, we show that benefits are achieved when considering the top 10 consuming Network Slice (NS). In addition, we could observe mobile service similarities in the spatial domain, while the spectral and time domains open the door for wavelets uncertainty, where we point future directions to address this research branch. Moreover, we propose two data-driven algorithms that shed light on the trade-off between complexity and multiplexing efficiency derived from the network slice specifications, both exhibiting promising performances (e.g., leading to a new architecture for traffic balancing in the cloud and edge clusters, with 60% and 400% gain in efficiency respectively and 1/3 of dedicated resources).Telematics EngineeringUniversidad Carlos III de Madrid, SpainpubBanchs, Albert20202020-11-27doctoral thesishttp://purl.org/coar/resource_type/c_db06VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/doctoralThesishttp://hdl.handle.net/20.500.12761/900reponame:IMDEA Networks Institute Digital Repositoryinstname:IMDEA Networks InstituteInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dspace.networks.imdea.org:20.500.12761/9002026-06-06T12:35:51Z
dc.title.none.fl_str_mv Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
title Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
spellingShingle Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
Márquez, Cristina|||0000-0002-2924-4362
Big Data
Network slicing
Resource Management
Network Efficiency
Mobile Networks
Slice Orchestration
NFV
title_short Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
title_full Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
title_fullStr Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
title_full_unstemmed Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
title_sort Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
dc.creator.none.fl_str_mv Márquez, Cristina|||0000-0002-2924-4362
author Márquez, Cristina|||0000-0002-2924-4362
author_facet Márquez, Cristina|||0000-0002-2924-4362
author_role author
dc.contributor.none.fl_str_mv Banchs, Albert
dc.subject.none.fl_str_mv Big Data
Network slicing
Resource Management
Network Efficiency
Mobile Networks
Slice Orchestration
NFV
topic Big Data
Network slicing
Resource Management
Network Efficiency
Mobile Networks
Slice Orchestration
NFV
description Several businesses are nowadays becoming more and more aware of the potential that lies beneath Big Data. From social media ‘titans’ and healthcare companies, to the mobile industry that propelled them, 5th Generation mobile network (5G) will be a fundamental factor that will drive our new reality. Current mobile service usage has been explored for data-driven organizational growth in the touristic sector, as it allows forecasting hotel occupancy rates or targeting customers. However, mobile data is continuously increasing, and therefore it is enormously important to analyze such data for networking purposes. Previous Ericsson Mobility Reports claimed that by the end of 2022 the total monthly traffic associated with mobile devices would be 77 exabytes (EB), representing 20% of the total Internet Protocol (IP) traffic around the world. They also declare that 50 EB/month would come from 2nd Generation mobile network (2G), 3rd Generation mobile network (3G), and 4th Generation mobile network (4G) devices. In terms of 5G subscriptions, it is expected to reach up to 2.8 billion subscriptions globally by the end of 2025, accounting for about 30% of total mobile subscriptions. Experts stake out that by the year 2020, 1.7 megabytes of data will be generated every second for every person on the planet, forcing the network to evolve and adapt to challenging new demands. Network providers not only deal with the deployment of the required resources to support this growth, but also the potential newcomers into the business, and the stringent conditions of the distinct services to be provided. One of the features proposed to face this dilemma is using the network slicing technique. It allows to transform and orchestrate a 5G network by creating multiple logical instances (i.e., slices) on top of it, while Big Data would provide the specifications of the services’ traffic dynamics to be served. In this way, operators achieve the best allocation of resources. This thesis contributes to the ongoing Network Slicing research, assessing a nationwide scenario. Our results show mobile traffic similarities and differences across time, space, and frequency domains, whereas we intend for distinct service clusterizations that would enhance the network efficiency in terms of resource management. For instance, we show that benefits are achieved when considering the top 10 consuming Network Slice (NS). In addition, we could observe mobile service similarities in the spatial domain, while the spectral and time domains open the door for wavelets uncertainty, where we point future directions to address this research branch. Moreover, we propose two data-driven algorithms that shed light on the trade-off between complexity and multiplexing efficiency derived from the network slice specifications, both exhibiting promising performances (e.g., leading to a new architecture for traffic balancing in the cloud and edge clusters, with 60% and 400% gain in efficiency respectively and 1/3 of dedicated resources).
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-11-27
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12761/900
url http://hdl.handle.net/20.500.12761/900
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
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
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:IMDEA Networks Institute Digital Repository
instname:IMDEA Networks Institute
instname_str IMDEA Networks Institute
reponame_str IMDEA Networks Institute Digital Repository
collection IMDEA Networks Institute Digital Repository
repository.name.fl_str_mv
repository.mail.fl_str_mv
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