Virtual machine placement is one of the core problems of resource scheduling in cloud data center. It has an important impact on the performance, resource utilization and energy consumption of data center. In order to optimize the data center energy consumption, improve resource utilization and ensure Quality of Service (QoS), a fuzzy membership degree based virtual machine placement algorithm was proposed. Firstly, combined the overload probability of physical hosts with the fitness placement relationship between virtual machines and physical hosts, a new distance measurement method was proposed. Then, according to the fuzzy membership function, the fitness fuzzy membership matrix between virtual machines and physical hosts was calculated. Finally, with the mechanism of energy awareness, the local search was performed in the fuzzy membership matrix to obtain the optimal placement scheme of the migration virtual machines. Simulation results show that the proposed algorithm can reduce the energy consumption of cloud data center, improve resource utilization and ensure QoS.

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.

The existing authentication schemes based on user social relations have the problems that the user trust computation is not reasonable, the identity voucher is lack of authentication weight and the authentication threshold cannot change with the change of user familiarity. In order to solve these problems, a user social relation-based mobile terminal authentication scheme in cloud computing environment was proposed. Firstly, the user trust was calculated from two aspects of communication trust and attribute trust. And then, the dynamic weights and dynamic authentication thresholds of identity vouchers were set according to user familiarity. Finally, the generation and certification processes of identity vouchers were improved. The experimental results show that the proposed scheme not only solves the problems in the existing authentication scheme based on user social relations, but also reduces the resource consumption of the mobile terminals, which is only a third of the existing methods. Therefore, the proposed scheme is more suitable for the mobile cloud computing environment.

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.

Aiming at object classification problem in heavily crowded and complex visual surveillance scenes, a real-time object classification approach was proposed based on discriminable features and continuous tracking. Firstly rapid features matching including color, shape and position was utilized to build the initial target correspondence in the whole scene, in which motion direction and velocity of the moving target were used to predict the preferable searching area in the next frame to accelerate the target matching process. And then the appearance model was utilized to rematch the occluded object without establishing the correspondence. In order to enhance the classification precision, the final object classification results were determined by the maximum probability of continuous object feature extraction and classification according to the tracking results. Experimental results show that the proposed method gets better classification precision compared with the method which do not utilized the continuous tracking，and its correct rate averagely reaches 97%. The new scheme effectively improves the performance of object classification in the complex scenes.