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Paper ID: 2848

Improving Grid computing performance prediction using weighted templates
Ariel,Goyeneche Gabor,Terstyanszky Thierry,Delaitre Stephen,Winter

Appeared in: Proceedings of the UK e-Science All Hands Conference 2007 website: http://www.allhands.org.uk/2007/
Page Numbers:
Publisher: National e-Science Centre
Year: 2007
ISBN/ISSN: 978-0-9553988-3-4
Contributing Organisation(s):
Field of Science: e-Science

URL: http://www.allhands.org.uk/2007/proceedings/papers/865.pdf

Abstract: Understanding the performance behavior of Grid components to predict future Job submissions is considered one of the answers to automatically select computational resources to match users’ requirements maximizing its usability. Job characterization and similarity are key components in making a more accurate prediction. The purpose of this paper is to test how current data mining and statistical solutions that define jobs similarity perform in production Grid environments and to present a new method that defines template using two set of characteristics with different priorities and weights the templates prediction accuracy level for future use. The results show that the new method achieves in average a prediction errors than is 54 percent lower than those achieved by using dynamic templates

Keywords: e-Science, AHM 2007


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Last Updated: 22 Jun 12 11:02
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