Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/337751
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dc.date.accessioned2021-08-26T03:58:54Z-
dc.date.available2021-08-26T03:58:54Z-
dc.identifier.urihttp://hdl.handle.net/10603/337751-
dc.description.abstractVirtualized resource allocation to cloud users in accordance with their requirement is a pivotal step for newlineapplications deployment. To handle continuously changing workload on cloud infrastructure is the newlinecomplex task. During provisioning, the major concern is that if demand is low, excess of resources are newlineavailable which leads to over provisioning and if demand is high, the resources may not be enough, newlinewhich leads to under provisioning and poor QoS (Quality of Services). In case of cloud environment, newlineresources are provided on demand. Efficient resource provisioning needs a proactive approach which newlinecan predict the future load before provision of resources to reduce the under or over provisioning newlineproblem. The above-mentioned challenge has to be handled through proactive resource provisioning newlineapproach, which can predict the future demands of resources. This approach helps in deploying and newlineprovisioning of resources efficiently based on the demands without loss of QoS. A prediction model newlinereleases the unused resources from the pool of resources by maintaining Quality of Services (QoS) for newlineresource provisioning in advance. To reduce the latency and improve the performance of cloud, newlineaccurate workload prediction strategy for provisioning of resources efficiently is the dominant aspect in newlinethe cloud-based services. In our research work, used model predict the future workload to consider the newlinearriving requests from cloud servers for resource provisioning efficiently. A prediction model which can newlinepredict the resource demands in advance for dynamic resource provisioning from the observed or newlinehistoric database in a virtualized environment is proposed. The prediction model ARIMA-PERP newline(Autoregressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning) newlineevaluated the implementation so as to satisfy the on-demand need of end users for efficient resource newlineutilization. The accuracy of prediction model is assessed for proposed QoS parameters and the crossvalidation newlinemethod. newlineThe proposed approaches
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dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titlePerformance Analysis of Adaptive Cloud through Workload Prediction Supporting Resource Provisioning
dc.title.alternative
dc.creator.researcherGadhavi, Lataben
dc.subject.keywordARIMA-PERP
dc.subject.keywordAutoregressive
dc.subject.keywordQuality of Service
dc.description.note
dc.contributor.guideBhavsar, Madhuri
dc.publisher.placeAhmedabad
dc.publisher.universityNirma University
dc.publisher.institutionInstitute of Technology
dc.date.registered2012
dc.date.completed2019
dc.date.awarded2021
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Institute of Technology

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02. certificate.pdf63.5 kBAdobe PDFView/Open
03. declaration.pdf68.1 kBAdobe PDFView/Open
04_declaration.pdf63.62 kBAdobe PDFView/Open
05_acknowledgement.pdf64.97 kBAdobe PDFView/Open
06_contents.pdf83.19 kBAdobe PDFView/Open
07_list_of_tables.pdf63.44 kBAdobe PDFView/Open
08_list_of_figures.pdf92.94 kBAdobe PDFView/Open
10_chapter_1.pdf758.8 kBAdobe PDFView/Open
11_chapter_2.pdf312.4 kBAdobe PDFView/Open
12_chapter_3.pdf143.43 kBAdobe PDFView/Open
13_chapter_4.pdf739.35 kBAdobe PDFView/Open
14_chapter_5.pdf758.09 kBAdobe PDFView/Open
15_chapter_6.pdf5.45 MBAdobe PDFView/Open
16_conclusion.pdf81.15 kBAdobe PDFView/Open
17_references.pdf124.9 kBAdobe PDFView/Open
80_recommendation.pdf226.33 kBAdobe PDFView/Open
abstract.pdf26.99 kBAdobe PDFView/Open


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