Dynamic resource provisioning in IaaS cloud environment

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorRaivio, Yrjö
dc.contributor.authorMallavarapu, Ramasivakarthik
dc.contributor.departmentTietotekniikan laitosfi
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorYlä-Jääski, Antti
dc.date.accessioned2020-12-28T10:22:27Z
dc.date.available2020-12-28T10:22:27Z
dc.date.issued2012
dc.description.abstractElasticity is one of the key-enablers of cloud systems, minimizing the cost of resource provisioning while meeting critical Quality of Service (QoS) requirements of a service level agreement (SLA). Most internet based services have SLA's that demand stringent performance requirements. Automated resource provisioning (AutoScaling) is an effective way of dealing with workload fluctuations by allocating resources based on the current demand. Simple reactive approaches to AutoScaling can have a contrasting effect on performance, while over-provisioning substantially increases the costs. To tackle these challenges, there is a need for intelligent resource provisioning mechanisms that can model, analyse and predict the resource demand. This thesis outlines the key practical issues involved in AutoScaling in an Infrastructure as a Service (IaaS) cloud environment and provides tangible solutions. We study a few prediction models and make a comparative analysis on their strengths and weaknesses. We then present the predictive elastic resource controller that addresses the issues in AutoScaling by using modelling techniques from statistical analysis. The research also identifies issues relating to resource demand and capacity estimation in a multi-tenant cloud environment. A prototype model of the predictive resource controller was implemented on an OpenNebula based cluster. Real world and artificial workload traces were used to test the efficiency of the model. We have also made a comparative analysis of our proposed model with a simple, reactive resource controller. Simulation results show that our model outperforms a simple, reactive resource controller in. terms of prediction error, QoS and number of SLA violations.en
dc.format.extent[9] + 46
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/100186
dc.identifier.urnURN:NBN:fi:aalto-2020122859017
dc.language.isoenen
dc.programme.majorTietokoneverkotfi
dc.programme.mcodeT-110fi
dc.rights.accesslevelclosedAccess
dc.subject.keywordclouden
dc.subject.keywordautoscalingen
dc.subject.keywordIaaS clouden
dc.subject.keywordworkload modelingen
dc.subject.keywordSLAen
dc.subject.keywordtime series analysisen
dc.titleDynamic resource provisioning in IaaS cloud environmenten
dc.type.okmG2 Pro gradu, diplomityö
dc.type.ontasotMaster's thesisen
dc.type.ontasotPro gradu -tutkielmafi
dc.type.publicationmasterThesis
local.aalto.digiauthask
local.aalto.digifolderAalto_02825
local.aalto.idinssi45215
local.aalto.openaccessno
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