Efficient fault monitoring with Collaborative Prediction

Dawei Feng 1 Cecile Germain-Renaud 2 Tristan Glatard 3
2 Collaboration Grid Observatory
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%.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00766086
Contributor : Ccsd Sciencesconf.Org <>
Submitted on : Monday, December 17, 2012 - 3:10:16 PM
Last modification on : Wednesday, December 12, 2018 - 3:27:34 PM
Long-term archiving on : Sunday, December 18, 2016 - 3:27:40 AM

Identifiers

  • HAL Id : hal-00766086, version 1

Citation

Dawei Feng, Cecile Germain-Renaud, Tristan Glatard. Efficient fault monitoring with Collaborative Prediction. journées scientifiques mésocentres et France Grilles 2012, Oct 2012, Paris, France. ⟨hal-00766086⟩

Share

Metrics

Record views

421

Files downloads

98