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A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

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A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

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dc.contributor.author Canovas Solbes, Alejandro es_ES
dc.contributor.author REGO MAÑEZ, ALBERT es_ES
dc.contributor.author Romero Martínez, José Oscar es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2021-03-06T04:32:05Z
dc.date.available 2021-03-06T04:32:05Z
dc.date.issued 2020-01-15 es_ES
dc.identifier.issn 1084-8045 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163292
dc.description.abstract [EN] Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture. es_ES
dc.description.sponsorship This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formation del Profesorado Universitario FPU (Convocatoria 2015)", grant number FPU15/06837 and by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigation Cientffica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Network and Computer Applications es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Software defined network (SDN) es_ES
dc.subject Machine learning es_ES
dc.subject QoE es_ES
dc.subject Traffic multimedia pattern es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jnca.2019.102498 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU15%2F06837/ES/FPU15%2F06837/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84802-C2-1-P/ES/RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Canovas Solbes, A.; Rego Mañez, A.; Romero Martínez, JO.; Lloret, J. (2020). A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN. Journal of Network and Computer Applications. 150:1-14. https://doi.org/10.1016/j.jnca.2019.102498 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jnca.2019.102498 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 150 es_ES
dc.relation.pasarela S\409952 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
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