It is difficult for the traditional Sparse Representation Classification (SRC) algorithm to enhance the performance of human activity recognition because of ignoring the correlation structure information hidden in sparse coefficient vectors of the test sample. To address this problem, a block sparse model-based human activity recognition approach was proposed. The human activity recognition problem was considered as a sparse representation-based classification problem on the basis of the inherent sparse block structure in human activity pattern. The block sparse Bayesian learning algorithm was used to solve the optimal sparse representation coefficients of a test sample for a linear combination of the training samples from the same class, and then the reconstruction residual of sparse coefficients was defined to determine the class of the test sample, which effectively improved the recognition rate of human activity pattern. The USC-HAD database containing different styles of human daily activity was selected to evaluate the effectiveness of the proposed approach. The experimental results show that the activity recognition rate of the proposed approach reaches 97.86%, which is increasd by 5% compared to the traditional human activity methods. These results demonstrate that the proposed method can effectively capture the discriminative information of the different activity pattern, and significantly improve the accuracy of human activity recognition.

Dynamic consolidation of Virtual Machine (VM) is a promising solution to address the energy inefficiency of data centers. This paper focused on VM placement and its optimization. First, in order to improve the energy efficiency, a CPU utilization-based best fit decreasing algorithm was presented to complete the VM placement. However, due to the variability of workloads experienced by applications, the VM placement should be optimized continuously in an online manner. Therefore, a threshold-based active VM migration mechanism was proposed to solve the dynamic optimization. Extensive simulation results show the proposed algorithms can significantly reduce the energy consumption and the number of VM migrations, while keeping the metrics of Performance Degradation due to Migration (PDM) and Overload Time per Active Server (OTAS) in low level.