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

中文翻译：

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利用AR预测模型的云环境中动态负载平衡的平衡资源利用（BRU）方法

为了避免因临时峰值负载AR时间序列预测模型而产生不必要的迁移。作者的方法是处理负载平衡问题。从实际角度出发，并使用KVM虚拟机管理程序在OpenStack云中实施。此外，提议的方法可以解决异构环境中VM迁移的问题。环境。KEywDS自动回归（AR）模型，CPU负载，CPU利用率，节能，响应时间，虚拟机，警告阈值