摘要
In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utilization is essential to improve energy efficiency of cloud data center.Although,most of the existing literature focuses on virtual machine(VM)consolidation for increasing energy efficiency at the cost of service level agreement degradation.In order to improve the existing approaches,load aware three-gear THReshold(LATHR)as well as modified best fit decreasing(MBFD)algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA.It offers promising results under dynamic workload and variable number of VMs(1-290)allocated on individual host.The outcomes of the proposed work are measured in terms of SLA,energy consumption,instruction energy ratio(IER)and the number of migrations against the varied numbers of VMs.From experimental results it has been concluded that the proposed technique reduced the SLA violations(55%,26%and 39%)and energy consumption(17%,12%and 6%)as compared to median absolute deviation(MAD),inter quartile range(IQR)and double threshold(THR)overload detection policies,respectively.
随着在线工作量的激增,云用户呈指数级增长。然而,云环境下需求下单大规模数据中心将导致高能耗。因此,优化资源利用对于提高云数据中心的能效至关重要。现有文献大多关注虚拟机(VM)整合,以简化服务水平协议为代价来提高能效。本文提出了负载感知的三齿轮阈值(LATHR)和改进的最佳拟合减少(MBFD)算法,在提高服务质量的同时,最大限度地降低总能耗。在单个主机上分配的动态工作负载和可变数量的虚拟机(1-290)下,提供了有效结果。实验结果通过SLA、能量消耗、指令能量比(IER)以及相对于不同虚拟机数量的迁移次数来衡量。实验结果表明,与中位数绝对偏差(MAD)、四分位范围(IQR)和双阈值(THR)过载检测策略相比,该技术SLA违反率分别降低了55%、26%和39%,能耗分别降低了17%、12%和6%。
