A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN
| Autor: | Alejandro Canovas, Oscar Romero, Albert Rego, Jaime Lloret |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Traffic multimedia pattern
Multimedia Artificial neural network Computer Networks and Communications Computer science media_common.quotation_subject Quality of service Node (networking) 020206 networking & telecommunications 02 engineering and technology INGENIERIA TELEMATICA computer.software_genre Computer Science Applications Software defined network (SDN) Hardware and Architecture Machine learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Quality of experience QoE Software-defined networking computer media_common Jitter |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| DOI: | 10.1016/j.jnca.2019.102498 |
| Popis: | [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. 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. |
| Databáze: | OpenAIRE |
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