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.

Aiming at the low detection rate caused by data imbalance in Android malware detection, an Android malware detection model based on Bagging-SVM (Support Vector Machine) integrated algorithm was proposed. Firstly, the permission information, intent information and component information were extracted as features from the file AndroidManifest.xml. Secondly, IG-ReliefF hybrid selection algorithm was proposed to reduce the dimension of data sets, and multiple balanced data sets were formed by bootstrap sampling method. Finally, a Bagging-based SVM ensemble classifier was trained by the multiple balanced data sets to detect Android malware. In the classification experiment, the detection rates of Bagging-SVM and random forest algorithm were 99.4% when the number of benign and malicious samples was balanced. When the ratio of benign and malicious samples was 4:1, the detection rate of Bagging-SVM algorithm was 6.6% higher than random forest algorithm and AdaBoost algorithm without reducing the detection accuracy. The experiment results show that the proposed model still has high detection rate and classification accuracy and can detect the vast majority of malware in the case of data imbalance.

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.

Energy efficient routing is a challenging problem in resource constrained Mobile Wireless Sensor Network (MWSN). Focused on the issue that the energy consumption of the inter-community routing in the mobile sensor network is too fast, an Energy-balanced Routing Algorithm for Inter-community (ERAI) was proposed. In ERAI, a new routing metric FC (Forwarding Capacity) based on the residual energy of nodes and the probability of encounter was designed. Then, this metric FC and the directional information of encountered nodes were used for selection of a relay node to forward the messages. The experimental data show that the death time of the first node of ERAI was later than that of Epidemic and PROPHET by 12.6%-15.6% and 4.5%-8.3% respectively, and the residual energy mean square deviation of ERAI was less than that of Epidemic and PROPHET. The experimental results show that the ERAI can balance the energy consumption of each node to a certain extent, and thus prolongs the network lifetime.

Responding to the problem of the unreasonable distribution of cluster head nodes and "hot spots" caused by uneven load energy in Wireless Sensor Network (WSN), an Unequal partition Area Uneven Clustering routing protocol (UAUC) was proposed. The network was divided according to unequal partition area, and the appropriate cluster head nodes in each area were selected on the basis of the energy factor, the distance factor and the intensity factor. Meanwhile, a load balancing path tree was built between cluster head nodes to solve the problem of "hot spots" in data transmission. In the comparison experiments with LEACH (Low Energy Adaptive Clustering Hierarchy) protocol, DEBUC (Distributed Energy-Balanced Unequal Clustering routing) protocol and HRPNC (Hierarchical Routing Protocol based on Non-uniform Clustering) protocol, UAUC achieved more reasonable distribution of cluster head nodes. The network cycle of UAUC was increased than that of LEACH, DEBUC and HRPNC by 88%, 12% and 17.5% respectively. The average residual energy of UAUC was higher than LEACH, DEBUC and HRPNC. And the variance of node residual energy of UAUC was less than LEACH, DEBUC and HRPNC. What is more, the aggregate of data packet of UAUC was higher than that of LEACH, DEBUC and HRPNC by 400%, 87.5% and 17.5% respectively. The experimental results show that UAUC can effectively improve the energy efficiency and the aggregate of data packet, balance energy consumption and prolong the network lifetime.

Concerning the problems of low accuracy, limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm (AFSA), a Parallel Dynamic weigh Niches Artificial Fish Swarm (PDN-AFS) algorithm based on multi-core cluster was proposed. Firstly, the advantages and disadvantages of AFSA were analyzed, and dynamic weighting factor strategy and niche mechanism were adopted, hence a new Dynamic weigh Niches Artificial Fish Swarm (DN-AFS) algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model (MPI+OpenMP) were introduced. Finally, the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem, and achieve high speed ratio.