Fang Qiu, Gong Qi, Wang Jun, Wang Yaonan
National Engineering Laboratory for Robot Vision Perception and Control Technology, Department of Control Science and Engineering, Hunan University, Changsha, Hunan, 410082, PR China.
Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA 95064, USA.
ISA Trans. 2019 Jul;90:202-212. doi: 10.1016/j.isatra.2018.12.038. Epub 2019 Jan 4.
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.
本文聚焦于降低高性能计算数据中心的能耗问题，尤其是那些存在大量“小尺寸”作业的中心。与先前的工作不同，作业调度和处理的效率被置于首要位置。为了在保持作业处理效率的同时降低服务器能耗，提出了一种新的滞后计算资源供应算法，以被动地调整总计算资源。提出了一个动态热模型来反映计算系统和冷却系统之间的关系。所提出的模型用于制定约束最优控制问题，以最小化冷却系统的能耗。然后，提出了一种两步解决方案。首先，开发了一种热感知资源分配优化器，以决定资源应在何处增加或减少。其次，设计了一种经济模型预测控制器，以随着机架功率的变化预测性地调整冷却温度。通过使用真实作业轨迹进行模拟，研究了所提方法的性能。结果表明，在所保证的服务质量下，可以实现显著的节能效果。
