TY - GEN
T1 - CloudSocket
T2 - 34th IEEE International Conference on Computer Design, ICCD 2016
AU - Lee, Seil
AU - Kim, Hanjoo
AU - Park, Seongsik
AU - Kim, Seijoon
AU - Choe, Hyeokjun
AU - Jeong, Chang Sung
AU - Yoon, Sungroh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/22
Y1 - 2016/11/22
N2 - Today's datacenters are equipped with diverse computing and storage devices for handling a myriad of data and normally consume a significant amount of electrical energy. This paper proposes a smart grid inspired methodology to monitor and profile the energy consumption of a datacenter, with the aim of providing information useful for reducing the peak power consumption of the datacenter. Our energy measurement platform is named CloudSocket, and each CloudSocket unit can measure the power consumption of an individual computing node and periodically transmit the measurement information wirelessly to the coordinator unit that can manage many Cloud-Sockets simultaneously. We tested our methodology with a 32-node grid system that runs Apache Spark for large-scale data analytics. Analyzing our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informative coordination of the computing components for overall power reduction.
AB - Today's datacenters are equipped with diverse computing and storage devices for handling a myriad of data and normally consume a significant amount of electrical energy. This paper proposes a smart grid inspired methodology to monitor and profile the energy consumption of a datacenter, with the aim of providing information useful for reducing the peak power consumption of the datacenter. Our energy measurement platform is named CloudSocket, and each CloudSocket unit can measure the power consumption of an individual computing node and periodically transmit the measurement information wirelessly to the coordinator unit that can manage many Cloud-Sockets simultaneously. We tested our methodology with a 32-node grid system that runs Apache Spark for large-scale data analytics. Analyzing our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informative coordination of the computing components for overall power reduction.
UR - http://www.scopus.com/inward/record.url?scp=85006758485&partnerID=8YFLogxK
U2 - 10.1109/ICCD.2016.7753322
DO - 10.1109/ICCD.2016.7753322
M3 - Conference contribution
AN - SCOPUS:85006758485
T3 - Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
SP - 436
EP - 439
BT - Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 October 2016 through 5 October 2016
ER -
