Optimization based resource and cooling management for a high performance computing data center.

This paper focuses on the problem of reducing energy consumption within high-performance computing data centers, especially for those with a large portion of "small size" jobs. Different from previous works, the efficiency of job scheduling and processing is made as the first priority. To reduce energy from servers while maintaining the processing efficiency of jobs, a new hysteresis computing resource-provisioning algorithm is proposed to adjust the total computing resource reactively. A dynamical thermal model is presented to reflect the relationship between the computational system and cooling system. The proposed model is used to formulate constrained optimal control problems to minimize the energy consumption of the cooling system. Then, a two-step solution is proposed. Firstly, a thermal-aware resource allocation optimizer is developed to decide where the resource should be increased or decreased. Secondly, an economic model predictive controller is designed to adjust the cooling temperature predictively along with the variation of the rack power. Performance of the proposed method is studied through simulations with real job trace. The results show that significant energy saving can be achieved with guaranteed service quality. • On the basis of scheduling and processing jobs efficiently, a control framework is proposed for HPC data centers to coordinate hysteresis resource provisioning, thermal-aware allocation and dynamic cooling management. • An economic model predictive control based technique for the modeling and managing the thermal environment. • Comparison of proposed method is made with existing techniques. The proposed methodology achieves significant energy saving, and low performance loss in service quality. [ABSTRACT FROM AUTHOR]