基于负载不确定性的虚拟机整合方法

物理主机工作负载的不确定性容易造成物理主机过载和资源利用率低，从而影响数据中心的能源消耗和服务质量。针对该问题，通过分析物理主机的工作负载记录与虚拟机资源请求的历史数据，提出了基于负载不确定性的虚拟机整合（WU-VMC）方法。为了稳定云数据中心各主机的工作负载，该方法首先利用虚拟机的资源请求拟合物理主机工作负载，并利用梯度下降方法计算虚拟机与物理主机的虚拟机匹配度；然后，利用匹配度进行虚拟机整合，从而解决负载不确定造成的能耗增加和服务质量下降等问题。仿真实验结果表明，WU-VMC方法降低了数据中心的能源消耗，减少了虚拟机迁移次数，提高了数据中心的资源利用率及服务质量。

Workload uncertainty-based virtual machine consolidation method

The uncertainty of workload in physical hosts easily leads to high overloaded risk and low resource utilization in physical hosts, which will further affect the energy consumption and service quality of data center. In order to solve this problem, a Workload Uncertainty-based Virtual Machine Consolidation (WU-VMC) method was proposed by analyzing the workload records of physical hosts and the historical data of virtual machine resource request. In order to stabilize the workload of each host in the cloud data center, firstly, the workloads of physical hosts were fitted according to resource requests of virtual machines, and the virtual machine matching degree between virtual machines and physical hosts was computed by using gradient descent method. Then, the virtual machines were integrated by using the matching degree to solve the problems such as increased energy consumption and decreased service quality which were caused by uncertain load. The simulation experimental results show that the proposed WU-VMC method can decrease energy consumption and virtual machine migration times of data center, improving the resource utilization and service quality of data center.