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dc.contributor.authorCasellas Regi, Ramón
dc.contributor.authorMartínez Rivera, Ricardo Victor
dc.contributor.authorVelasco Esteban, Luis Domingo
dc.contributor.authorVilalta, Ricard
dc.contributor.authorPavón Mariño, Pablo
dc.contributor.authorKing, Daniel
dc.contributor.authorMuñoz González, Raül
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2019-05-02T06:18:55Z
dc.date.available2019-05-02T06:18:55Z
dc.date.issued2018
dc.identifier.citationCasellas, R. [et al.]. Enabling data analytics and machine learning for 5G services within disaggregated multi-layer transport networks. A: International Conference on Transparent Optical Networks. "ICTON 2018: 20th International Conference on Transparent Optical Networks: 1-5 July 2018, Bucharest, Romania". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-4.
dc.identifier.isbn978-1-5386-6605-0
dc.identifier.urihttp://hdl.handle.net/2117/132462
dc.description.abstractRecent advances, related to the concepts of Artificial Intelligence (AI) and Machine Learning (ML) and with applications across multiple technology domains, have gathered significant attention due, in particular, to the overall performance improvement of such automated systems when compared to methods relying on human operation. Consequently, using AI/ML for managing, operating and optimizing transport networks is increasingly seen as a potential opportunity targeting, notably, large and complex environments.Such AI-assisted automated network operation is expected to facilitate innovation in multiple aspects related to the control and management of future optical networks and is a promising milestone in the evolution towards autonomous networks, where networks self-adjust parameters such as transceiver configuration.To accomplish this goal, current network control, management and orchestration systems need to enable the application of AI/ML techniques. It is arguable that Software-Defined Networking (SDN) principles, favouring centralized control deployments, featured application programming interfaces and the development of a related application ecosystem are well positioned to facilitate the progressive introduction of such techniques, starting, notably, in allowing efficient and massive monitoring and data collection.In this paper, we present the control, orchestration and management architecture designed to allow the automatic deployment of 5G services (such as ETSI NFV network services) across metropolitan networks, conceived to interface 5G access networks with elastic core optical networks at multi Tb/s. This network segment, referred to as Metro-haul, is composed of infrastructure nodes that encompass networking, storage and processing resources, which are in turn interconnected by open and disaggregated optical networks. In particular, we detail subsystems like the Monitoring and Data Analytics or the in-operation planning backend that extend current SDN based network control to account for new use cases.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
dc.subject.lcshOptical communications
dc.subject.lcshTelecommunication -- Traffic
dc.subject.lcshMachine learning
dc.subject.otherData analytics
dc.subject.otherSDN/NFV control of optical/multi-layer networks
dc.titleEnabling data analytics and machine learning for 5G services within disaggregated multi-layer transport networks
dc.typeConference report
dc.subject.lemacComunicacions òptiques
dc.subject.lemacTelecomunicació -- Tràfic
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.identifier.doi10.1109/ICTON.2018.8473832
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8473832
dc.rights.accessOpen Access
local.identifier.drac23526869
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/761727/EU/METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency/METRO-HAUL
local.citation.authorCasellas, R.; Martínez, R.; Velasco, L.; Vilalta, R.; Pavón, P.; King, D.; Muñoz, R.
local.citation.contributorInternational Conference on Transparent Optical Networks
local.citation.publicationNameICTON 2018: 20th International Conference on Transparent Optical Networks: 1-5 July 2018, Bucharest, Romania
local.citation.startingPage1
local.citation.endingPage4


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