NDLI: Increasing large-scale data center capacity by statistical power control

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Increasing large-scale data center capacity by statistical power control

Content Provider	ACM Digital Library
Author	Zhu, Yinghang Jin, Xin Yang, Wenjun Hu, Dianming Xu, Wei Shi, Weisong Luo, Bing Huang, Longbo Wang, Shuhao Wang, Guosai
Abstract	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.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISBN	9781450342407
DOI	10.1145/2901318.2901338
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Power provisioning Power control Data center Scheduling
Content Type	Text
Resource Type	Article

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