Virtual Machine Placement (VMP) is the core of virtual machine consolidation and is a multi-objective optimization problem with multiple resource constraints. Efficient VMP algorithm can significantly reduce energy consumption, improve resource utilization, and guarantee Quality of Service (QoS). Concerning the problems of high energy consumption and low resource utilization in data center, a Discrete Bat Algorithm-based Virtual Machine Placement (DBA-VMP) algorithm was proposed. Firstly, an optimization model with multi-object constraints was established for VMP, with minimum energy consumption and maximum resource utilization as optimization objectives. Then, the pheromone feedback mechanism was introduced in the bat algorithm by emulating the pheromone sharing mechanism of artificial ant colonies in the foraging process, and the bat algorithm was improved and discretized. Finally, the improved discrete bat algorithm was used to solve the Pareto optimal solutions of the model. The experimental results show that compared with other multi-objective optimization algorithms for VMP, the proposed algorithm can effectively reduce energy consumption and improve resource utilization, and achieves an optimal balance between reducing energy consumption and improving resource utilization under the premise of guaranteeing QoS.

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.