基于Kalman滤波的云数据中心能耗和性能优化

云数据中心虚拟机的动态整合需要跟踪服务器的运行状态，而服务器的运行状态会受到数据中心负载变化的影响，现有的CPU使用率预测方法大都只关注当前服务器的CPU利用率变化。提出了一个基于Kalman滤波的CPU使用率预测模型，建立了基于所有服务器CPU使用率变化系数的数据中心负载变化模型，详细描述了基于Kalman滤波的CPU使用率预测方法，讨论了云数据中心的能耗和性能评价指标。最后，为了验证基于Kalman滤波的CPU使用率预测算法的有效性，在CloudSim仿真系统和PlanetLab的五个数据集上进行了实验。实验结果表明，Kalman滤波能够较好地反映服务器CPU使用率的变化趋势，有效地降低数据中心的能耗，并保持较好的计算性能。

Energy and performance optimization based on Kalman filtering in the cloud data center

It is necessary to follow the running state of the servers for the virtual machine dynamical consolidation in cloud data centers, and the running state of the server can be affected by the load change of the cloud data center. Most of existing methods only concern the CPU utilization change of the current server. We propose a CPU utilization prediction model based on the Kalman filtering, formulate a load variation model of the cloud data center based on the variation coefficient of the CPU utilization of all servers, and then describe the prediction procedure based on the Kalman filtering in detail. Moreover, the energy consumption and performance evaluation of the cloud data center are discussed. Finally, we conduct experiments on the CloudSim with five workloads of PlanetLab. Experimental results show that the Kalman filtering can better reflect the change tendency of the CPU utilization, and maintain better computational performance with lower energy consumption.