云数据中心高效的虚拟机整合方法

High efficient virtual machines consolidation method in cloud data center

针对传统虚拟机整合(VMC)方法难以保持主机工作负载长期稳定的问题,提出一种基于高斯混合模型的高效虚拟机整合(GMM-VMC)方法。为了准确地预测主机负载的变化趋势,首先,使用高斯混合模型(GMM)对活动物理主机的工作负载历史记录进行拟合;然后,根据活动物理主机工作负载的GMM和主机自身的资源配置情况计算主机的过载概率,并根据过载概率判定主机是否存在过载风险;对存在过载风险的物理主机,根据部署在该物理主机上的虚拟机对降低主机过载风险的贡献和虚拟机迁移所需的时间这两个指标进行待迁移虚拟机选择;最后,使用GMM估算待迁移虚拟机对各个目标主机过载风险的影响,并选择受影响最小的主机作为目标主机。通过Cloud Sim仿真平台模拟该GMM-VMC方法,并根据能源消耗、服务质量(QoS)、整合效率等指标与已有的整合方法进行对比,实验结果表明,GMM-VMC方法能够有效地降低数据中心能耗,提高服务质量。

Concerning the problem that the workload of hosts in data center cannot maintain long-term stability by executing traditional Virtual Machine Consolidation( VMC), a high efficient Gaussian Mixture Model-based VMC( GMMVMC) method was proposed. Firstly, to accurately predict the variation trend of workload in hosts, Gaussian Mixture Model( GMM) was used to fit the workload history of hosts. Then, the overload probability of a host was calculated according to the GMM of its workload and resource capacity. Next, the aforementioned overload probability was taken as the criteria to determine whether the host is overloaded or not. Besides, some virtual machines hosted by overloaded hosts which can significantly degrade overload risk and demand less migration time were selected to migrate. At last, these migrated virtual machines were placed in new hosts which have less effect on workload variation after placement estimated by GMM. Using Cloud Sim toolkit, GMM-VMC method was validated and compared with other methods on energy consumption, Quality of Service( QoS) and efficiency of consolidation. The experimental results show that the GMM-VMC method can degrade energy consumption in data center and improve QoS.