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Article Dans Une Revue Journal of Grid Computing Année : 2011

Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients

Résumé

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 Cloud client application traces. We also present an overall evaluation of this approach , its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

Dates et versions

hal-01426826 , version 1 (05-01-2017)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Eddy Caron, Frédéric Desprez, Adrian Muresan. Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients. Journal of Grid Computing, 2011, 9, pp.49 - 64. ⟨10.1007/s10723-010-9178-4⟩. ⟨hal-01426826⟩
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