Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds

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dc.contributor.authorKenga, Derdus
dc.contributor.authorOmwenga, Vincent
dc.contributor.authorOgao, Patrick
dc.date.accessioned2022-12-26T11:41:35Z
dc.date.available2022-12-26T11:41:35Z
dc.date.issued2021
dc.description.abstractThe main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilizationen_US
dc.identifier.citationKenga, D., Omwenga, V., & Ogao, P. (2021). Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds. Journal of Information Technology and Computer Science, 6(2), 170-182.en_US
dc.identifier.urihttps://jitecs.ub.ac.id/index.php/jitecs/article/view/196
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/6566
dc.language.isoenen_US
dc.publisherJournal of Information Technology and Computer Scienceen_US
dc.subjectVirtual machinesen_US
dc.subjectCloud computingen_US
dc.subjectData centre energy consumptionen_US
dc.subjectVirtual machine auto-scalingen_US
dc.subjectCloudSim Plusen_US
dc.titleVirtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Cloudsen_US
dc.typeArticleen_US
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