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Discovering Piecewise Linear Models of Grid Workload

Tamas Elteto 1 Cecile Germain-Renaud 1, 2 Pascal Bondon 3 Michèle Sebag 1, 2
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 : Despite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. This study is based on exhaustive information about the gLite-monitored jobs from the EGEE grid, representative of a significant fraction of e-science computing activity in Europe. Our main contributions are twofold. First we found that workload models for this grid can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we present a bootstrapping strategy for building more robust models from the limited samples at hand.
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Submitted on : Saturday, June 12, 2010 - 8:36:54 PM
Last modification on : Thursday, June 17, 2021 - 3:47:24 AM
Long-term archiving on: : Thursday, June 30, 2011 - 1:00:05 PM


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  • HAL Id : hal-00491562, version 1



Tamas Elteto, Cecile Germain-Renaud, Pascal Bondon, Michèle Sebag. Discovering Piecewise Linear Models of Grid Workload. 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, May 2010, Melbourne, Australia. pp.474-484. ⟨hal-00491562⟩



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