Balance Resource Utilization (BRU) Approach for the Dynamic Load Balancing in Cloud Environment by Using AR Prediction Model

Oneofthemajorchallengesforthecloudprovideristheefficientutilizationofthephysicalresources. Toachievethis,thispaperproposedaBalanceResourceUtilization(BRU)approachthatnotonly minimizestheresourceleakagebutalsoincreasestheresourceutilizationandoptimizethesystem performance.Theproposedapproachconsider tworesources i.e.,CPUandmemory,asdecision metrics for loadbalancinganduse three thresholdsnamed lower threshold,upper thresholdand warningthresholdtodefineunderloaded,overloadedandwarningsituations,respectively.Themain conceptof thisapproach is toplaceVMto thePM,whereresourcerequirementof theVMand resourceutilizationofthePMarecomplementstoeachother.Toevadeunnecessarymigrationsdue tothetemporarypeakloadARtimeseriespredictionmodelisused.Theauthors’approachtreatsload balancingproblemfromthepracticalperspectiveandimplementedinOpenStackcloudwithKVM hypervisor.Moreover,proposedapproachresolvetheissueofVMmigrationintheheterogeneous environment. KEywORDS Auto Regression (AR) Model, CPU Load, CPU Utilization, Energy Efficient, Response Time, Virtual Machine, Warning Threshold

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