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dc.contributor.authorMestres Sugrañes, Albert
dc.contributor.authorAlarcón Cot, Eduardo José
dc.contributor.authorCabellos Aparicio, Alberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2018-10-29T18:53:07Z
dc.date.issued2018
dc.identifier.citationMestres, A., Alarcon, E., A. C. A machine learning-based approach for virtual network function modeling. A: IEEE Wireless Communications and Networking Conference Workshops. "2018 IEEE Wireless Communications and Networking Conference Workshops: (WCNCW 2018) Barcelona, Spain 15-18 April 2018". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 237-241.
dc.identifier.isbn978-1-5386-4069-2
dc.identifier.urihttp://hdl.handle.net/2117/123193
dc.description.abstractRecent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. The objective of this paper is to explore the feasibility of applying different models and ML techniques to model complex networks elements, such as Virtual Network Functions (VNFs). In particular, we focus on the characterization of the CPU consumption of the VNF as a function of the characteristics of the input traffic. The traffic is represented by a set of features describing characteristics from the transport layer to the application layer in small time batches. The CPU consumption is observed from the hypervisor and corresponds to the average CPU consumption when the traffic batch is processed. We experimentally demonstrate that we can learn the behavior of different VNF in order to model its CPU consumption. We conclude that the behavior of different VNF can be modeled using ML techniques. © 2018 IEEE.
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::Telemàtica i xarxes d'ordinadors
dc.subject.lcshWireless LANs
dc.subject.otherArtificial intelligence
dc.subject.otherComplex networks
dc.subject.otherE-learning
dc.subject.otherLearning algorithms
dc.subject.otherNetwork function virtualization
dc.subject.otherTransfer functions
dc.subject.otherWireless telecommunication systems
dc.subject.otherApplication layers
dc.subject.otherHypervisor
dc.subject.otherInput traffic
dc.subject.otherRecent trends
dc.subject.otherTransport layers
dc.subject.otherVirtual networks
dc.subject.otherLearning systems
dc.titleA machine learning-based approach for virtual network function modeling
dc.typeConference report
dc.subject.lemacXarxes locals sense fil Wi-Fi
dc.contributor.groupUniversitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
dc.contributor.groupUniversitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits
dc.identifier.doi10.1109/WCNCW.2018.8369019
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8369019
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23409051
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/2PE/TEC2017-90034-C2-1-R
dc.date.lift10000-01-01
upcommons.citation.authorMestres, A., Alarcon, E., Albert Cabellos-Aparicio
upcommons.citation.contributorIEEE Wireless Communications and Networking Conference Workshops
upcommons.citation.publishedtrue
upcommons.citation.publicationName2018 IEEE Wireless Communications and Networking Conference Workshops: (WCNCW 2018) Barcelona, Spain 15-18 April 2018
upcommons.citation.startingPage237
upcommons.citation.endingPage241


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