@conference{ArozarenaWNR2011,
author = "Arozarena, Pablo and Toribio, Raquel and Carrera Barroso, {\'A}lvaro",
abstract = "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.",
booktitle = "International Workshop on Network Resilience: From Research to Practice",
doi = "10.1109/SRDSW.2011.20",
isbn = "978-0-7695-4451-9",
keywords = "Bayesian inference,distributed fault diagnosis,outer edge,agents,self-learning",
series = "30th International Symposium on Reliable Distributed Systems",
title = "{D}istributed {F}ault {D}iagnosis using {B}ayesian {R}easoning in {MAGNETO}",
year = "2011",
}