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Cluster Computing and the Grid, IEEE International Symposium on (2010)
Melbourne, VIC, Australia
May 17, 2010 to May 20, 2010
ISBN: 978-0-7695-4039-9
pp: 474-484
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.
Grid models, segmentation, time-series

T. Élteto, M. Sebag, P. Bondon and C. Germain-Renaud, "Discovering Piecewise Linear Models of Grid Workload," Cluster Computing and the Grid, IEEE International Symposium on(CCGRID), Melbourne, VIC, Australia, 2010, pp. 474-484.
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