Abstract: For issue of Virtual Machine （VM） migration in cloud computing environment when it comes to meeting the demands of load balancing, auto scaling, green energy-saving etc, this paper designs a scheduling algorithm for dynamic consolidation of virtual machines based on Brown’s cubic Exponential Smoothing （ES）. By organically integrating the maximum correlation policy and the algorithm denoted Power Aware Best Fit Decreasing （PABFD）, the proposed algorithm achieves dynamic balance of the load of entire data center, as well as with the exponential smoothing prediction model to predict the workload condition in future time. The simulation results show that the algorithm can reduce the energy consumption of data centers and Service Level Agreement violations, effectively increasing the overall resource utilization of data center as the core of the cloud infrastructure.

Key words: cloud computing, virtual machine, exponential smoothing, dynamic consolidation

摘要： 针对云计算环境下满足负载均衡、自动伸缩、绿色节能等需求时所面临的虚拟机（VM）迁移问题，提出一种基于布朗指数平滑法的虚拟机动态整合方法（ES）。利用指数平滑模型预测未来时刻的负载情况，结合最大相关性策略和能源感知最佳适应降序算法（PABFD），实现主机负载的动态平衡。仿真结果显示该方法能够减少数据中心的能源消耗和SLA违例次数，有效提升云基础设施整体资源利用率。

关键词: 云计算, 虚拟机, 指数平滑法, 动态整合