摘要：

为能在保证服务质量的前提下提高数据中心能源利用率，提出一种基于用户访问量预测的数据中心虚拟机自适应节能机制，根据自适应Holt-Winters(AHW)预测法研究互联网用户访问行为的周期性，使其能根据用户访问量自适应地调整虚拟机数量以提高虚拟机的利用率，达到减少数据中心能耗的目的。仿真实验结果显示，AHW预测法最高平均绝对百分误差为22.46%，基于AHW预测法的数据中心虚拟机利用率为97.88%，相比未采用节能机制时提高了37.19%，从而证明该节能机制对周期性用户访问进行预测时具有较好的统计性能和较强的鲁棒性，能更好地满足数据中心节能的需求。

关键词: 云计算, 数据中心, 虚拟机, 节能, 自适应Holt-Winters预测, 极大似然估计

Abstract:

In order to improve the energy utilization rate in data center on the premise of guaranteeing Quality of Service(QoS), this paper proposes a data center Virtual Machine(VM) adaptive energy-saving mechanism based on the prediction of users’ access quantity, and researches periodicity of users’ visit by Adaptive Holt-Winters(AHW) prediction method. It can adaptively adjust the number of VM according to user visits to improve the utilization rate of VM and achieve the purpose of reducing data center energy consumption. Simulation experimental results show that the Mean Absolute Percentage Error(MAPE) of AHW method is 22.46% and the utilization rate of VM in data center is 97.88% which is promoted by 37.19% compared with the former utilization without using this adaptive energy-saving mechanism, and proves that this energy-saving mechanism has good statistical properties and stability for forecasting the periodic user access, it can be better satisfy the needs of data center energy-efficient.

Key words: cloud computing, data center, Virtual Machine(VM), energy-saving, Adaptive Holt-Winters(AHW) prediction, Maximum Likelihood Estimation(MLE)

中图分类号: 

• TP311