Since the early Mild Cognitive Impairment (MCI) is very likely to be undiagnosed by the assessment of medical diagnostic cognitive scale, a multimodal network fusion method for the aided diagnosis and classification of MCI was proposed. The complex network analysis method based on graph theory has been widely used in the field of neuroimaging, but different effects of brain diseases on the network topology of the brain would be conducted by using imaging technologies based different modals. Firstly, the Diffusion Tensor Imaging (DTI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data were used to construct the fusion network of brain function and structure connection. Then, the topological properties of the fusion network were analyzed by One-way ANalysis of VAriance (ANOVA), and the attributes with significant difference were selected as the classification features. Finally, the one way cross validation of Support Vector Machines (SVM) was used for the classification of healthy group and MCI group, and the accuracy was estimated. The experimental results show that, the classification result accuracy of the proposed method reaches 94.44%, which is significantly higher than that of single modal data method. Many brain regions, such as cingulate gyrus, superior temporal gyrus and parts of the frontal and parietal lobes, of the MCI patients diagnosed by the proposed method show significant differences, which is basically consistent with the existing research results.

With the increasing energy consumption in current data centers, many emerging energy-saving mechanisms have been proposed to reduce the energy consumption, but most of these methods assume data center is in a homogeneous environment. However, most of current data centers are heterogeneous as different types of servers are purchased at different time in reality. An energy-efficient method named Primary Virtual Machine Allocation Policy (PVMAP) was proposed, with the performance/power introduced as a parameter to indicate the energy efficiency of each server. The server of high energy efficiency would be fully utilized with high priority in the dynamic Virtual Machine (VM) consolidation. Also the consolidation process would try to minimize the VM migrations and running hosts in the end. The simulation results demonstrate that the PVMAP can guarantee the energy conservation and Quality of Service (QoS) at the same time, and it has better stability and extensibility.