Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching

Eddy Caron 1 Frédéric Desprez 2 Adrian Muresan 1, 3
2 GRAAL - Algorithms and Scheduling for Distributed Heterogeneous Platforms
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
3 AVALON - Algorithms and Software Architectures for Distributed and HPC 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. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world traces from Cloud and Grid platforms. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.
Keywords : Cloud Computing SPADES
Document type :
Conference papers
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https://hal.inria.fr/hal-00758592
Contributor : Eddy Caron <>
Submitted on : Thursday, November 29, 2012 - 2:22:10 AM
Last modification on : Friday, April 20, 2018 - 3:44:26 PM

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Eddy Caron, Frédéric Desprez, Adrian Muresan. Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching. IEEE CloudCom 2010, Nov 2010, Indianapolis, Indiana, USA, United States. ⟨10.1109/CloudCom.2010.65⟩. ⟨hal-00758592⟩

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