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.

Concerning the problem that the workload of hosts in data center cannot maintain long-term stability by executing traditional Virtual Machine Consolidation (VMC), a high efficient Gaussian Mixture Model-based VMC (GMM-VMC) method was proposed. Firstly, to accurately predict the variation trend of workload in hosts, Gaussian Mixture Model (GMM) was used to fit the workload history of hosts. Then, the overload probability of a host was calculated according to the GMM of its workload and resource capacity. Next, the aforementioned overload probability was taken as the criteria to determine whether the host is overloaded or not. Besides, some virtual machines hosted by overloaded hosts which can significantly degrade overload risk and demand less migration time were selected to migrate. At last, these migrated virtual machines were placed in new hosts which have less effect on workload variation after placement estimated by GMM. Using CloudSim toolkit, GMM-VMC method was validated and compared with other methods on energy consumption, Quality of Service (QoS) and efficiency of consolidation. The experimental results show that the GMM-VMC method can degrade energy consumption in data center and improve QoS.

Considering the uncertainty of dynamic workloads in cloud computing, an Virtual Machine (VM) dynamic consolidation method based on adaptive overloaded threshold selection was proposed. In order to make a trade-off between energy efficiency and Quantity of Services (QoS) of data centers, an adaptive overloaded threshold selection problem model based on Markov decision processes was designed. The optimal decision was calculated by solving this problem model, and the overloaded threshold was dynamically adjusted by using the optimal decision according to energy efficiency and QoS of data center. Overloaded threshold was used to predict overloaded hosts and trigger VM migrations. According to the principle of minimum migration time and minimum energy consumption growth, the VM migration strategy under overloaded threshold constraint was given, and the underloaded hosts were switched to sleep mode. Simulation results show that this method can significantly avoid excessive virtual machine migrations and decrease the energy consumption while improving QoS effectively; in addition, it can achieve an ideal balance between QoS and energy consumption of data center.