Probabilistic fault diagnosis in the MAGNETO autonomic control loop

Pablo Arozarena, Raquel Toribio, Jesse Kielthy, Kevin Quinn, Martin Zach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Management of outer edge domains is a big challenge for service providers due to the diversity, heterogeneity and large amount of such networks, together with limited visibility on their status. This paper focuses on the probabilistic fault diagnosis functionality developed in the MAGNETO project, which enables finding the most probable cause of service problems and thus triggering appropriate repair actions. Moreover, its self-learning capabilities allow continuously enhancing the accuracy of the diagnostic process.

Original languageEnglish
Title of host publicationMechanisms for Autonomous Management of Networks and Services - 4th International Conference on Autonomous Infrastructure, Management and Security, AIMS 2010, Proceedings
Pages102-105
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event4th International Conference on Autonomous Infrastructure, Management and Security, AIMS 2010 - Zurich, Switzerland
Duration: 23 Jun 201025 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6155 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Autonomous Infrastructure, Management and Security, AIMS 2010
Country/TerritorySwitzerland
CityZurich
Period23/06/201025/06/2010

Keywords

  • Autonomic
  • Bayesian Network
  • Home Area Networks (HAN)
  • Probabilistic Management
  • Self-learning

Fingerprint

Dive into the research topics of 'Probabilistic fault diagnosis in the MAGNETO autonomic control loop'. Together they form a unique fingerprint.

Cite this