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

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 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.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01426826
Contributor : Eddy Caron <>
Submitted on : Thursday, January 5, 2017 - 9:12:14 AM
Last modification on : Friday, April 20, 2018 - 3:44:26 PM

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Links full text

Identifiers

Collections

Citation

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

Share

Metrics

Record views

331