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Forecasting for Cloud computing on-demand resources based on pattern matching

Eddy Caron 1, 2 Frédéric Desprez 1, 2 Adrian Muresan 2 
2 GRAAL - Algorithms and Scheduling for Distributed Heterogeneous Platforms
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. Knowledge in advance is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose a new approach to the problem of workload prediction based on identifying similar past occurrences to the current short-term workload history. We present in detail the auto-scaling algorithm that uses the above approach as well as experimental results by using real-world data and an overall evaluation of this approach, its potential and usefulness.
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Reports (Research report)
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Submitted on : Wednesday, October 19, 2011 - 4:15:18 PM
Last modification on : Wednesday, October 26, 2022 - 8:16:27 AM
Long-term archiving on: : Sunday, December 4, 2016 - 7:10:45 PM


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  • HAL Id : inria-00460393, version 3



Eddy Caron, Frédéric Desprez, Adrian Muresan. Forecasting for Cloud computing on-demand resources based on pattern matching. [Research Report] RR-7217, INRIA. 2010, pp.26. ⟨inria-00460393v3⟩



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