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.

Since it is necessary to evaluate and analyze the service performance of cloud computing center to guarantee Quality of Service (QoS) and avoid violation of Service Layer Agreement (SLA), a approximated analysis model based on M/M/n/n+r queue theory was proposed for cloud computing center. By solving this model the probability distribution function of response time and other QoS indicators were acquired, meanwhile the relationship among the number of servers, size of queue buffers, response time, blocking probability and instance service probability were revealed and verified by simulation.The experimental results indicate that improving server service rate is better than increasing the number of servers for improving service performance.