Optimera användningen av virtuella maskiner i Azure med maskininlärning

dc.contributor.authorHEDBERG GRIFFITH, KEVIN
dc.contributor.authorNGUYEN, ERIK
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:38:33Z
dc.date.available2019-07-03T14:38:33Z
dc.date.issued2016
dc.description.abstractThis report describes a project that is about examining the possibilities to optimize the utilization of virtual machines in Microsoft Azure using machine learning. The thesis has been done at the company Atea Global Services (AGS). Virtual machines is a Azure service that AGS uses. However could these virtual machines be running without really being used. Services in Azure are not for free and when using a service like virtual machines companies are being charged be per minute. This means that AGS pays unnecessary expenses for the virtual machines that are running when they are not being used. Using an Azure service called Machine Learning Studio, a user pattern for when a virtual machine was being used was developed. An application has been developed that turns on or off a virtual machine based on user patterns from Machine Learning Studio. AGS can choose whether they want to continue working on the project or to take advantage of it right away to cut back on costs.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/252442
dc.language.isoswe
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleOptimera användningen av virtuella maskiner i Azure med maskininlärning
dc.type.degreeExamensarbete på grundnivåsv
dc.type.uppsokM
local.programmeDatateknik 180 hp (högskoleingenjör)
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