Soft Failure Detection Using Factorial Hidden Markov Models

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-noticed for 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 behaviour of the overall infrastructure, and we identify such deviations with soft failures, or, in an...

• Type: Conference
• Year: 2007
• Where: ICMLA
• Authors: Guillaume Bouchard, Jean-Marc Andreoli
• Keywords: Factorial Hidden Markov, ICMLA 2007, Machine Learning, Potential Productivity Loss, Soft Failures