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Machine learning for network automation: Overview, architecture, and applications [invited tutorial]
dc.contributor.author | Rafique, Danish |
dc.contributor.author | Velasco Esteban, Luis Domingo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2018-11-29T08:29:18Z |
dc.date.available | 2018-11-29T08:29:18Z |
dc.date.issued | 2018 |
dc.identifier.citation | Rafique, D., Velasco, L. Machine learning for network automation: Overview, architecture, and applications [invited tutorial]. "Journal of optical communications and networking", 2018, vol. 10, núm. 10, p. D126-D143. |
dc.identifier.issn | 1943-0620 |
dc.identifier.uri | http://hdl.handle.net/2117/125214 |
dc.description.abstract | Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications. With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica |
dc.subject.lcsh | Optical communications |
dc.subject.lcsh | Machine learning |
dc.subject.other | Analytics |
dc.subject.other | Artificial intelligence |
dc.subject.other | Autonomous networking |
dc.subject.other | Big data |
dc.subject.other | Communication networks |
dc.subject.other | Optical fiber communication |
dc.subject.other | Telemetry |
dc.title | Machine learning for network automation: Overview, architecture, and applications [invited tutorial] |
dc.type | Article |
dc.subject.lemac | Comunicacions òptiques |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques |
dc.identifier.doi | 10.1364/JOCN.10.00D126 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.osapublishing.org/jocn/abstract.cfm?URI=jocn-10-10-D126 |
dc.rights.access | Open Access |
local.identifier.drac | 23518489 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/761727/EU/METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency/METRO-HAUL |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-90097-R/ES/COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION/ |
local.citation.author | Rafique, D.; Velasco, L. |
local.citation.publicationName | Journal of optical communications and networking |
local.citation.volume | 10 |
local.citation.number | 10 |
local.citation.startingPage | D126 |
local.citation.endingPage | D143 |
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