A telecom analytics framework for dynamic quality of service management

Mozo Velasco, Bonifacio Alberto and Guadamillas Herranz, Álvaro and López, Miguel Ángel and Pulvirenti, Fabio and Maravitsas, Nikolaos (2014). A telecom analytics framework for dynamic quality of service management. In: "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.

Description

Title: A telecom analytics framework for dynamic quality of service management
Author/s:
  • Mozo Velasco, Bonifacio Alberto
  • Guadamillas Herranz, Álvaro
  • López, Miguel Ángel
  • Pulvirenti, Fabio
  • Maravitsas, Nikolaos
Item Type: Presentation at Congress or Conference (Lecture)
Event Title: First International Workshop on Big Data Applications and Principles
Event Dates: 11/09/2014 - 12/09/2014
Event Location: Madrid, Spain
Title of Book: First International Workshop on Big Data Applications and Principles : Proceedings
Date: September 2014
ISBN: 978-84-15302-94-0
Subjects:
Freetext Keywords: Machine learning, Big Data, traffic characterization, quality of service, Quality of Experience
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 35324
DC Identifier: http://oa.upm.es/35324/
OAI Identifier: oai:oa.upm.es:35324
Official URL: http://ict-ontic.eu/bigdap14/bigdap14_PROCEEDINGS.pdf
Deposited by: Memoria Investigacion
Deposited on: 18 Mar 2016 18:52
Last Modified: 18 Mar 2016 18:52
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