A telecom analytics framework for dynamic quality of service management

Mozo Velasco, Bonifacio Alberto; Guadamillas Herranz, Álvaro; López, Miguel Ángel; Pulvirenti, Fabio y Maravitsas, Nikolaos (2014). A telecom analytics framework for dynamic quality of service management. En: "First International Workshop on Big Data Applications and Principles", 11/09/2014 - 12/09/2014, Madrid, Spain. ISBN 978-84-15302-94-0. pp. 103-132.

Descripción

Título: A telecom analytics framework for dynamic quality of service management
Autor/es:
  • Mozo Velasco, Bonifacio Alberto
  • Guadamillas Herranz, Álvaro
  • López, Miguel Ángel
  • Pulvirenti, Fabio
  • Maravitsas, Nikolaos
Tipo de Documento: Ponencia en Congreso o Jornada (Lectura)
Título del Evento: First International Workshop on Big Data Applications and Principles
Fechas del Evento: 11/09/2014 - 12/09/2014
Lugar del Evento: Madrid, Spain
Título del Libro: First International Workshop on Big Data Applications and Principles : Proceedings
Fecha: Septiembre 2014
ISBN: 978-84-15302-94-0
Materias:
Palabras Clave Informales: Machine learning, Big Data, traffic characterization, quality of service, Quality of Experience
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications.

Más información

ID de Registro: 35324
Identificador DC: http://oa.upm.es/35324/
Identificador OAI: oai:oa.upm.es:35324
URL Oficial: http://ict-ontic.eu/bigdap14/bigdap14_PROCEEDINGS.pdf
Depositado por: Memoria Investigacion
Depositado el: 18 Mar 2016 18:52
Ultima Modificación: 18 Mar 2016 18:52
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