云计算环境下基于需求预测的虚拟机节能分配方法研究

Energy Efficient Allocation of Virtual Machines in Cloud Computing Environments Based on Demand Forecast

在云计算环境中,大量用来处理各种用户需求的虚拟机分布在具有相异物理配置的主机上.维持这些主机和配套设施的正常运转需要消耗大量的能源.为了控制云计算环境的运营支出并提高其能源利用率,提出了基于需求预测的虚拟机节能分配方法.首先,由于用户需求通常具有时变性且符合一定的季节性模型,所以利用Holt-Winters指数平滑法对后续周期的需求进行预测.其次,根据预测结果,利用修改后的背包算法在主机之间合理地分配虚拟机.最后,利用自优化模块对预测模型中的参数进行自适应更新,并确定合适的预测周期.实验表明该方法可以有效减少主机的开关机操作次数,从而降低云计算环境中无谓的能源消耗.

In cloud computing environments, demands from different users are often handled on virtual machines （VMs） which are deployed over plenty of hosts. Huge amount of electrical power is consumed by these hosts and auxiliary infrastructures that support them. However, demands are usually time-variant and of some seasonal pattern. It is possible to reduce power consumption by forecasting varying demands periodically and allocating VMs accordingly. In this paper, we propose a power-saving approach based on demand forecast for allocation of VMs. First of all, we forecast demands of next period with Holt-Winters＇ exponential smoothing method. Second, a modified knapsack algorithm is used to find the appropriate allocation between VMs and hosts. Third, a self-opti- mizing module updates the values of parameters in Holt-Winters＇ model and determines the reasonable forecast frequency. We carried out a set of experiments whose results indicate that our approach can reduce the frequency of switching on/off hosts. In comparison with other approaches, this method leads to considerable power saving for cloud computing environments.