TY - JOUR
T1 - Exploring customizable heterogeneous power distribution and management for datacenter
AU - Liu, Longjun
AU - Sun, Hongbin
AU - Li, Chao
AU - Hu, Yang
AU - Li, Tao
AU - Zheng, Nanning
N1 - Publisher Copyright:
© 1990-2012 IEEE.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Large-scale datacenters are facing increasing pressure of capping their carbon emission and power cost. Many leading-edge studies have started to explore server clusters running on multiple power sources. Existing approaches do not sufficiently consider the fine-grained power delivery to satisfy diverse requirements in datacenter, especially in the multi-tenant/colocation datacenter, which may yield low energy utilization. To address the emerging trend and new requirements, this article proposes a novel Datacenter inner Power Switch Network (DiPSN) to improve datacenter power efficiency and user satisfaction. DiPSN is a reconfigurable and easy-to-scale-out power architecture, which enables datacenter to distribute various power sources in a fine-grained manner. Moreover, a tailored machine learning based power source management framework is proposed for DiPSN to dynamically optimize user customized performance metrics and maximize datacenter revenue. Compared with conventional single-switch power distribution system, our DiPSN can be configured to improve solar energy utilization by 39.6 percent, reduce utility power cost by 11.1 percent and improve workload performance by 33.8 percent. Meanwhile, our design can extend battery lifetime by 9.3 percent. This work could provide valuable guidelines for designing heterogeneous power distribution architecture and management methodology in datacenters for improving user-customizable efficiency, sustainability and economy.
AB - Large-scale datacenters are facing increasing pressure of capping their carbon emission and power cost. Many leading-edge studies have started to explore server clusters running on multiple power sources. Existing approaches do not sufficiently consider the fine-grained power delivery to satisfy diverse requirements in datacenter, especially in the multi-tenant/colocation datacenter, which may yield low energy utilization. To address the emerging trend and new requirements, this article proposes a novel Datacenter inner Power Switch Network (DiPSN) to improve datacenter power efficiency and user satisfaction. DiPSN is a reconfigurable and easy-to-scale-out power architecture, which enables datacenter to distribute various power sources in a fine-grained manner. Moreover, a tailored machine learning based power source management framework is proposed for DiPSN to dynamically optimize user customized performance metrics and maximize datacenter revenue. Compared with conventional single-switch power distribution system, our DiPSN can be configured to improve solar energy utilization by 39.6 percent, reduce utility power cost by 11.1 percent and improve workload performance by 33.8 percent. Meanwhile, our design can extend battery lifetime by 9.3 percent. This work could provide valuable guidelines for designing heterogeneous power distribution architecture and management methodology in datacenters for improving user-customizable efficiency, sustainability and economy.
KW - Datacenters
KW - computer system implementation
KW - energy utilization
KW - power distribution architecture
KW - power management
UR - https://www.scopus.com/pages/publications/85047839789
U2 - 10.1109/TPDS.2018.2841405
DO - 10.1109/TPDS.2018.2841405
M3 - 文章
AN - SCOPUS:85047839789
SN - 1045-9219
VL - 29
SP - 2798
EP - 2813
JO - IEEE Transactions on Parallel and Distributed Systems
JF - IEEE Transactions on Parallel and Distributed Systems
IS - 12
M1 - 8368308
ER -