Efficient fault monitoring with Collaborative Prediction - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Other Publications Year : 2012

Efficient fault monitoring with Collaborative Prediction


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%.
Fichier principal
Vignette du fichier
mesogrilles_CPFDV2.pdf (637.85 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00758025 , version 1 (27-11-2012)




  • HAL Id : hal-00758025 , version 1


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


Gmail Facebook X LinkedIn More