Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Arozarena, Pablo; Toribio, Raquel y Carrera Barroso, Alvaro (2011). Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO. En: "30th IEEE Symposium on Reliable Distributed Systems Workshops", 04/10/2011 - 07/10/2011, Madrid, Spain. ISBN 978-0-7695-4451-9.

Descripción

Título: Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO
Autor/es:
  • Arozarena, Pablo
  • Toribio, Raquel
  • Carrera Barroso, Alvaro
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 30th IEEE Symposium on Reliable Distributed Systems Workshops
Fechas del Evento: 04/10/2011 - 07/10/2011
Lugar del Evento: Madrid, Spain
Título del Libro: Proceedings of 30th IEEE Symposium on Reliable Distributed Systems Workshops
Fecha: 2011
ISBN: 978-0-7695-4451-9
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería de Sistemas Telemáticos [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (2MB) | Vista Previa

Resumen

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

Más información

ID de Registro: 12213
Identificador DC: http://oa.upm.es/12213/
Identificador OAI: oai:oa.upm.es:12213
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6076793
Depositado por: Memoria Investigacion
Depositado el: 29 Ago 2012 08:05
Ultima Modificación: 21 Abr 2016 11:25
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM