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dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorYazidi, Anis
dc.contributor.authorBegnum, Kyrre
dc.date.accessioned2016-09-21T17:16:14Z
dc.date.accessioned2017-03-31T13:28:49Z
dc.date.available2016-09-21T17:16:14Z
dc.date.available2017-03-31T13:28:49Z
dc.date.issued2016-09-19
dc.identifier.citationJournal of Forecasting 2016language
dc.identifier.issn1099-131X
dc.identifier.urihttps://hdl.handle.net/10642/4635
dc.description.abstractIn a cloud environment virtual machines are created with different purposes like providing users with computers, handling web traffic etc. A virtual machine is created in such a way that a user will not notice any differences from working on a physical computer. A challenging problem in cloud computing is how to distribute the virtual machines on a set of physical servers. An optimal solution will provide each virtual machine with enough resources and at the same time not using more physical serves (energy/electricity) than necessary to achieve this. In this paper we investigate how forecasting of future resource re- quirements (CPU conspumption) for each virtual machine can be used to improve the virtual machine placement on the physical servers. We demonstrate that a time dependent Hidden Markov model with an autoregressive observation process replicates the properties the CPU consumption data in a realistic way and forecasts future CPU con- sumption efficiently.language
dc.language.isoenlanguage
dc.publisherWileylanguage
dc.rightsThis is the accepted version of the following article: Hammer, H. L., Yazidi, A., & Begnum, K. (2016). An Inhomogeneous Hidden Markov Model for Efficient Virtual Machine Placement in Cloud Computing Environments. Journal of Forecasting., which has been published in final form at http://dx.doi.org/10.1002/for.2441.language
dc.subjectCloud computinglanguage
dc.subjectCpu consumptionlanguage
dc.subjectHidden Markov modellanguage
dc.subjectStochastic bin packinglanguage
dc.titleAn inhomogeneous Hidden Markov model for efficient virtual machine placement in a cloud computing enviromentlanguage
dc.typeJournal article
dc.typePeer reviewedlanguage
dc.typeJournal article
dc.date.updated2016-09-21T17:16:14Z
dc.description.versionacceptedVersionlanguage
dc.identifier.doihttp://dx.doi.org/10.1002/for.2441
dc.identifier.cristin1383946


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