Intelligent Service Orchestration in Edge Cloud Networks

The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In pa...

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Detalles Bibliográficos
Autores: Zeydan, E, Mangues-Bafalluy, J, Turk, Y
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p6390
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6390
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119949680&doi=10.1109%2fMNET.101.2100214&partnerID=40&md5=2c92eab9bad396ddf79a95f0f9771555
Access Level:acceso abierto
Palabra clave:Mobile telecommunication systems
Wireless networks
Cloud networks
Data traffic
Edge clouds
Edge computing
High bandwidth
Intelligent Services
Network infrastructure
Service orchestration
Service requirements
Stringents
Network architecture
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spelling Intelligent Service Orchestration in Edge Cloud NetworksZeydan, EMangues-Bafalluy, JTurk, YMobile telecommunication systemsWireless networksCloud networksData trafficEdge cloudsEdge computingHigh bandwidthIntelligent ServicesNetwork infrastructureService orchestrationService requirementsStringentsNetwork architectureThe surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform. © 2021 IEEE.Institute of Electrical and Electronics Engineers Inc.2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6390https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119949680&doi=10.1109%2fMNET.101.2100214&partnerID=40&md5=2c92eab9bad396ddf79a95f0f9771555IEEE NetworkISSN: 08908044ISSNe: 1558156Xreponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p63902026-06-17T11:44:47Z
dc.title.none.fl_str_mv Intelligent Service Orchestration in Edge Cloud Networks
title Intelligent Service Orchestration in Edge Cloud Networks
spellingShingle Intelligent Service Orchestration in Edge Cloud Networks
Zeydan, E
Mobile telecommunication systems
Wireless networks
Cloud networks
Data traffic
Edge clouds
Edge computing
High bandwidth
Intelligent Services
Network infrastructure
Service orchestration
Service requirements
Stringents
Network architecture
title_short Intelligent Service Orchestration in Edge Cloud Networks
title_full Intelligent Service Orchestration in Edge Cloud Networks
title_fullStr Intelligent Service Orchestration in Edge Cloud Networks
title_full_unstemmed Intelligent Service Orchestration in Edge Cloud Networks
title_sort Intelligent Service Orchestration in Edge Cloud Networks
dc.creator.none.fl_str_mv Zeydan, E
Mangues-Bafalluy, J
Turk, Y
author Zeydan, E
author_facet Zeydan, E
Mangues-Bafalluy, J
Turk, Y
author_role author
author2 Mangues-Bafalluy, J
Turk, Y
author2_role author
author
dc.subject.none.fl_str_mv Mobile telecommunication systems
Wireless networks
Cloud networks
Data traffic
Edge clouds
Edge computing
High bandwidth
Intelligent Services
Network infrastructure
Service orchestration
Service requirements
Stringents
Network architecture
topic Mobile telecommunication systems
Wireless networks
Cloud networks
Data traffic
Edge clouds
Edge computing
High bandwidth
Intelligent Services
Network infrastructure
Service orchestration
Service requirements
Stringents
Network architecture
description The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform. © 2021 IEEE.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6390
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119949680&doi=10.1109%2fMNET.101.2100214&partnerID=40&md5=2c92eab9bad396ddf79a95f0f9771555
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6390
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119949680&doi=10.1109%2fMNET.101.2100214&partnerID=40&md5=2c92eab9bad396ddf79a95f0f9771555
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE Network
ISSN: 08908044
ISSNe: 1558156X
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
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