In view of the problems of term nesting, polysemy and error propagation between extraction subtask tasks, a deep attention model framework was proposed. First, the annotation strategy was used to jointly learn two sub tasks of knowledge extraction for solving the problem of error propagation. Second, a projection method combining multiple word vector information was proposed to alleviate the polysemy problem in term extraction in coal mine field. Third, a deep feature extraction network was designed, and a deep attention model and two model enhancement schemes were proposed to fully extract the semantic information. Finally, the classification layer of the model was analyzed to simplify the model to the maximum extent under the premise of ensuring the extraction effect. Experimental results show that, compared with the best model of coding-decoding structure, the proposed model has the F1-score increased by 1.5 percentage points and the model training speed improved by nearly 3 times. The proposed model can effectively complete two knowledge extraction subtasks which are term extraction and term relationship extraction in coal mine field.

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.