Optimera användningen av virtuella maskiner i Azure med maskininlärning

Examensarbete på grundnivå

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/252442
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Type: Examensarbete på grundnivå
Title: Optimera användningen av virtuella maskiner i Azure med maskininlärning
Authors: HEDBERG GRIFFITH, KEVIN
NGUYEN, ERIK
Abstract: This 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.
Keywords: Informations- och kommunikationsteknik;Data- och informationsvetenskap;Information & Communication Technology;Computer and Information Science
Issue Date: 2016
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/252442
Collection:Examensarbeten på grundnivå // Basic Level Theses



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