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Predicting Bounds on Queuing Delay in the EGEE grid

Julien Perez 1, 2, 3 Cecile Germain-Renaud 1, 2, 3 Balázs Kégl 4
1 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
Abstract : Predicting the performance of schedulers is a notoriously difficult task. As a consequence, grid users might be tempted to work around the standard grid middleware by designing specific strategies, which would be counterproductive if generally adopted. On the other hand, Machine Learning has been successfully applied to performance prediction in distributed and shared environments. This paper reports on experiments on predicting the basic parameters of scheduling in the EGEE framework.
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Submitted on : Saturday, September 22, 2007 - 11:49:53 PM
Last modification on : Thursday, July 8, 2021 - 3:48:43 AM
Long-term archiving on: : Friday, April 9, 2010 - 2:41:08 AM


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  • HAL Id : inria-00174290, version 1


Julien Perez, Cecile Germain-Renaud, Balázs Kégl. Predicting Bounds on Queuing Delay in the EGEE grid. 2nd EGEE User Forum, May 2007, Manchester, United Kingdom. ⟨inria-00174290⟩



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