DocumentCode :
3059504
Title :
Soft Failure Detection Using Factorial Hidden Markov Models
Author :
Bouchard, Guillaume ; Andreoli, Jean-Marc
fYear :
2007
fDate :
13-15 Dec. 2007
Firstpage :
160
Lastpage :
165
Abstract :
In modern business, educational, and other settings, it is common to provide a digital network that interconnects hardware devices for shared access by the users (e.g., in an office where printers are available for use by all the office workers). In such a context, so-called "soft" failures, where a device silently starts working in degraded mode, may easily go un-noticedfor a long time, resulting in potential productivity loss. It is therefore advantageous to enable system administrators to identify soft failures at an early stage. We propose here a probabilistic method using variational inference on a factorial hidden Markov model to automatically discover soft failures, based on the analysis of simple usage information which is normally logged by the network infrastructure. We propose to mine these logs in order to discover statistically significant deviations in the usage behavior of the overall infrastructure, and we identify such deviations with soft failures, or, in any case, situations of interest to an administrator.
Keywords :
computer network reliability; fault diagnosis; hidden Markov models; probability; variational techniques; digital network; factorial hidden Markov models; network infrastructure; probabilistic method; soft failure detection; usage behavior; variational inference; Degradation; Europe; Failure analysis; Floors; Hardware; Hidden Markov models; Information analysis; Machine learning; Printers; Productivity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
Conference_Location :
Cincinnati, OH
Print_ISBN :
978-0-7695-3069-7
Type :
conf
DOI :
10.1109/ICMLA.2007.79
Filename :
4457225
Link To Document :
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