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Efficient Distributed Monitoring with Active 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 Factorization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%. Comparison with other Collaborative Prediction strategies show that Active Probing is most efficient at dealing with the various sources of data variability.
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Submitted on : Sunday, February 3, 2013 - 11:53:21 AM
Last modification on : Thursday, July 8, 2021 - 3:47:54 AM
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Dawei Feng, Cecile Germain-Renaud, Tristan Glatard. Efficient Distributed Monitoring with Active Collaborative Prediction. Future Generation Computer Systems, Elsevier, 2013, 29 (8), pp.2272-2283. ⟨10.1016/j.future.2013.06.001⟩. ⟨hal-00784038⟩



Les métriques sont temporairement indisponibles