Aiming at the problem that the operation scale of Robust Non-negative Matrix Factorization (RNMF) increases with the number of training samples, an incremental robust non-negative matrix factorization algorithm with sparseness constraints was proposed. Firstly, robust non-negative matrix factorization was performed on initial data. Then, the factorized result participated in the subsequent iterative operation. Finally, with sparseness constraints, the coefficient matrix was combined with incremental learning, which made the objective function value fall faster in the iterative solution. The cost of computation was reduced and the sparseness of data after factorization was improved. In the numerical experiments, the proposed algorithm was compared with RNMF algorithm and RNMF with Sparseness Constraints (RNMFSC) algorithm. The experimental results on ORL and YALE face databases show that the proposed algorithm is superior to the other two algorithms in terms of operation time and sparseness of factorized data, and has better clustering effect, especially in YALE face database, when the clustering number is 3, the clustering accuracy of the proposed algorithm reaches 91.67%.

For the problem of optimizing resource allocation to achieve profit maximization of cloud computing center, an analysis model based on Service Layer Agreement (SLA)-aware was proposed for optimizing server number and speed of cloud center. Meanwhile some important factors were taken into account, such as energy cost, server rental cost, customer waiting time, and SLA violation penalty. The impacts of cloud center profit by changing server number and speed were analyzed by numerical simulation. The numerical simulation results indicate that cloud center will obtain maximum profit by optimizing server number and speed at a certain request rate; with request rate increasing, profit will increase linearly by optimizing server number and speed. The analysis results can provide a reference method for cloud service provider to improve net business gain.

For the problem of minimizing the energy consumption under performance constraint of cloud center, an optimal power consumption allocation method among multiple heterogeneous servers was proposed. First, an optimal energy consumption mathematical model of cloud center was built. Second, a Minimizing Power Consumption (MPC) algorithm for calculating the minimum energy was developed by using Lagrange multiplier method to obtain the optimal solution of the model. Finally, the MPC algorithm was verified by plenty of numerical experiments and compared with the Equal-Power (EP) baseline method. The experimental results indicate that MPC algorithm can save approximately 30% energy than the EP baseline method under the same load and the same response time conditions, and the proportion of energy saving will increase with load increasing. The MPC algorithm can effectively avoid energy configuration overload and it will provide ideas and reference data for optimal resource allocation of cloud center.

In order to reduce the test application time and guarantee high test data compression rate, a selection sequence of parallel folding counter was proposed. Selection test sequences were generated by recording group number and in-group number which represented folding index based on the analysis of parallel folding computing theory, so as to avoid generating useless and redundant test sequences. The experimental results on ISCAS benchmark circuits demonstrate the average test compression rate of the proposed scheme is 94.48%, and the average test application time is 15.31% of the similar scheme.