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dc.contributor.authorVelasco Esteban, Luis Domingo
dc.contributor.authorTabatabaeimehr, Fatemehsadat
dc.contributor.authorRuiz Ramírez, Marc
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.date.accessioned2020-10-22T07:40:16Z
dc.date.available2020-10-22T07:40:16Z
dc.date.issued2020
dc.identifier.citationVelasco, L.; Tabatabaeimehr, F.; Ruiz, M. Knowledge management in optical networks. A: International Conference on Transparent Optical Networks. "2020 22nd International Conference on Transparent Optical Networks (ICTON): July 19th-23rd, 2020: Bari, Italy". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1-5. ISBN 978-1-7281-8424-1. DOI 10.1109/ICTON51198.2020.9203235.
dc.identifier.isbn978-1-7281-8424-1
dc.identifier.urihttp://hdl.handle.net/2117/330608
dc.description©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractAutonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear thus leading into model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving models error convergence time, as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it.
dc.description.sponsorshipThe research leading to these results has received funding from the European Commission through the METROHAUL project (G.A. nº 761727), from the Spanish MINECO TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).
dc.format.extent5 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.lcshMachine learning
dc.subject.lcshOptical communications
dc.subject.lcshKnowledge management
dc.subject.otherNetwork automation
dc.subject.otherAutonomic transmission
dc.subject.otherSelf-learning
dc.titleKnowledge management in optical networks
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacComunicacions òptiques
dc.subject.lemacGestió del coneixement
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.identifier.doi10.1109/ICTON51198.2020.9203235
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9203235
dc.rights.accessOpen Access
local.identifier.drac29429383
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
dc.relation.projectidinfo: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.authorVelasco, L.; Tabatabaeimehr, F.; Ruiz, M.
local.citation.contributorInternational Conference on Transparent Optical Networks
local.citation.publicationName2020 22nd International Conference on Transparent Optical Networks (ICTON): July 19th-23rd, 2020: Bari, Italy
local.citation.startingPage1
local.citation.endingPage5


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