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Distributed Monitoring with Collaborative Prediction

Dawei Feng 1 Cecile Germain-Renaud 2, 1 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 Factor- ization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%.
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Submitted on : Wednesday, February 22, 2012 - 6:18:29 PM
Last modification on : Thursday, July 8, 2021 - 3:48:34 AM
Long-term archiving on: : Wednesday, December 14, 2016 - 8:03:36 AM


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  • HAL Id : hal-00673148, version 1


Dawei Feng, Cecile Germain-Renaud, Tristan Glatard. Distributed Monitoring with Collaborative Prediction. 12th IEEE International Symposium on Cluster, Cloud and Grid Computing (CCGrid'12), May 2012, Ottawa, Canada. epub ahead of print. ⟨hal-00673148⟩



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