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dc.contributor.advisorSommerville, Ian
dc.contributor.authorKhajeh-Hosseini, Ali
dc.coverage.spatial181en_US
dc.date.accessioned2013-03-21T14:08:00Z
dc.date.available2013-03-21T14:08:00Z
dc.date.issued2013-06-26
dc.identifieruk.bl.ethos.569022
dc.identifier.urihttp://hdl.handle.net/10023/3412
dc.description.abstractDecisions to deploy IT systems on public Infrastructure-as-a-Service clouds can be complicated as evaluating the benefits, risks and costs of using such clouds is not straightforward. The aim of this project was to investigate the challenges that enterprises face when making system deployment decisions in public clouds, and to develop vendor-neutral tools to inform decision makers during this process. Three tools were developed to support decision makers: 1. Cloud Suitability Checklist: a simple list of questions to provide a rapid assessment of the suitability of public IaaS clouds for a specific IT system. 2. Benefits and Risks Assessment tool: a spreadsheet that includes the general benefits and risks of using public clouds; this provides a starting point for risk assessment and helps organisations start discussions about cloud adoption. 3. Elastic Cost Modelling: a tool that enables decision makers to model their system deployment options in public clouds and forecast their costs. These three tools collectively enable decision makers to investigate the benefits, risks and costs of using public clouds, and effectively support them in making system deployment decisions. Data was collected from five case studies and hundreds of users to evaluate the effectiveness of the tools. This data showed that the cost effectiveness of using public clouds is situation dependent rather than universally less expensive than traditional forms of IT provisioning. Running systems on the cloud using a traditional 'always on' approach can be less cost effective than on-premise servers, and the elastic nature of the cloud has to be considered if costs are to be reduced. Decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Performing upfront cost modelling is beneficial as there can be significant cost differences between different cloud providers, and different deployment options within a single cloud. During such modelling exercises, the variations in a system's load (over time) must be taken into account to produce more accurate cost estimates, and the notion of elasticity patterns that is presented in this thesis provides one simple way to do this.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subjectCloud computingen_US
dc.subjectCloud adoptionen_US
dc.subjectSystem deploymenten_US
dc.subjectElasticityen_US
dc.subjectCost modellingen_US
dc.subjectRisk analysisen_US
dc.subject.lccQA76.585K5
dc.subject.lcshCloud computingen_US
dc.subject.lcshBusiness enterprises--Computer networks--Managementen_US
dc.subject.lcshCloud computing--Costs--Mathematical modelsen_US
dc.subject.lcshRisk assessmenten_US
dc.titleSupporting system deployment decisions in public cloudsen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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