Given the high cost of large-scale data centers, an important design goal is to fully utilize available power resources to maximize the computing capacity. In this paper we present Ampere, a novel power management system for data centers to increase the computing capacity by over-provisioning the number of servers. Instead of doing power capping that degrades the performance of running jobs, we use a statistical control approach to implement dynamic power management by indirectly affecting the workload scheduling, which can enormously reduce the risk of power violations. Instead of being a part of the already over-complicated scheduler, Ampere only interacts with the scheduler with two basic APIs. Instead of power control on the rack level, we impose power constraint on the row level, which leads to more room for over provisioning.   We have implemented and deployed Ampere in our production data center. Controlled experiments on 400+ servers show that by adding 17% servers, we can increase the throughput of the data center by 15%, leading to significant cost savings while bringing no disturbances to the job performance.
['Guosai Wang', 'Shuhao Wang', 'Bing Luo', 'Weisong Shi', 'Yinghang Zhu', 'Wenjun Yang', 'Dianming Hu', 'Longbo Huang', 'Xin Jin', 'Wei Xu']
Increasing large-scale data center capacity by statistical power control