Efficient fault monitoring with Collaborative Prediction

Dawei Feng 1 Cecile Germain-Renaud 1, 2 Tristan Glatard 3
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Isolating users from the inevitable faults in large distributed systems is critical to Quality of Experience. We formulate the problem of probe selection for fault prediction based on end-to-end probing as a Collaborative Prediction (CP) problem. On an extensive experimental dataset from the EGI grid, the combination of the Maximum Margin Matrix Factorization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%.
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https://hal.inria.fr/hal-00758025
Contributor : Cecile Germain <>
Submitted on : Tuesday, November 27, 2012 - 9:30:56 PM
Last modification on : Friday, October 26, 2018 - 10:41:42 AM
Long-term archiving on : Thursday, February 28, 2013 - 3:46:50 AM

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  • HAL Id : hal-00758025, version 1

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Dawei Feng, Cecile Germain-Renaud, Tristan Glatard. Efficient fault monitoring with Collaborative Prediction. 2012. ⟨hal-00758025⟩

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