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26880 Using Performance Forecasting to Accelerate Elasticity
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Moura, P. and Kon, F. and Voulgaris, S. and van Steen, M. (2015) Using Performance Forecasting to Accelerate Elasticity. In: Proceedings of the Second International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC 2015), Revised Selected Papers, 20 Jul 2015, Donostia, Spain. pp. 17-31. Lecture Notes in Computer Science 9438. Springer Verlag. ISSN 0302-9743 ISBN 978-3-319-28447-7

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Official URL: http://dx.doi.org/10.1007/978-3-319-28448-4_2

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Abstract

Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. In this context, monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we investigate how we can predict the performance of a service to dynamically adjust allocated resources based on predictions. In other words, instead of “repairing” because a threshold has been crossed, we attempt to stay ahead and allocate an optimized amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:COMMIT/EWIDS: Very large wireless sensor networks for well-being
Uncontrolled Keywords:cloud computing, elasticity, performance prediction, scalability
modeling
ID Code:26880
Status:Published
Deposited On:17 March 2016
Refereed:Yes
International:Yes
More Information:statisticsmetis

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