inria-00460393, version 3
Forecasting for Cloud computing on-demand resources based on pattern matching
Eddy Caron
a, 1, 2Frédéric Desprez b, 1, 2Adrian Muresan a, 2
N° RR-7217 (2010)
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.
- a – École normale supérieure de Lyon - ENS Lyon
- b – INRIA
- 1: Laboratoire de l'Informatique du Parallélisme (LIP)
- Université de Lyon – CNRS : UMR5668 – INRIA – École Normale Supérieure - Lyon – Université Claude Bernard - Lyon I
- 2: GRAAL (INRIA Grenoble Rhône-Alpes / LIP Laboratoire de l'Informatique du Parallélisme)
- CNRS : UMR5668 – INRIA – École Normale Supérieure - Lyon – Université Claude Bernard - Lyon I – Laboratoire d'informatique du Parallélisme
- Domain : Computer Science/Distributed, Parallel, and Cluster Computing
- Keywords : Cloud Computing – auto-scaling – pattern matching
- Internal note : RR-7217
- Available versions : v1 (2010-03-09) v2 (2010-07-02) v3 (2011-10-20)
- inria-00460393, version 3
- http://hal.inria.fr/inria-00460393
- oai:hal.inria.fr:inria-00460393
- From: Adrian Muresan
- Submitted on: Wednesday, 19 October 2011 16:15:18
- Updated on: Thursday, 20 October 2011 09:46:00
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