TY - GEN
T1 - Autonomic power & performance management for large-scale data centers
AU - Khargharia, Bithika
AU - Hariri, Salim
AU - Szidarovszky, Ferenc
AU - Houri, Manal
AU - El-Rewini, Hesham
AU - Khan, Samee Ullah
AU - Ahmad, Ishfaq
AU - Yousif, Mazin S.
PY - 2007
Y1 - 2007
N2 - With the rapid growth of servers and applications spurred by the Internet, the power consumption of servers has become critically important and must be efficiently managed. High energy consumption also translates into excessive heat dissipation which in turn, increases cooling costs and causes servers to become more prone to failure. This paper presents a theoretical and experimental framework and general methodology for hierarchical autonomic power & performance management in high performance distributed data centers. We optimize for power & performance (performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematicallyrigorous optimization approach to provide the application with the required amount of memory at runtime. This enables us to transition the unused memory capacity to a low power state. Our experimental results show a maximum performance/watt improvement of 88.48% compared to traditional techniques. We also present preliminary results of using Game Theory to optimize performance/watt at the cluster level of a data center. Our cooperative technique reduces the power consumption by 65% when compared to traditional techniques (min-min heuristic).
AB - With the rapid growth of servers and applications spurred by the Internet, the power consumption of servers has become critically important and must be efficiently managed. High energy consumption also translates into excessive heat dissipation which in turn, increases cooling costs and causes servers to become more prone to failure. This paper presents a theoretical and experimental framework and general methodology for hierarchical autonomic power & performance management in high performance distributed data centers. We optimize for power & performance (performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematicallyrigorous optimization approach to provide the application with the required amount of memory at runtime. This enables us to transition the unused memory capacity to a low power state. Our experimental results show a maximum performance/watt improvement of 88.48% compared to traditional techniques. We also present preliminary results of using Game Theory to optimize performance/watt at the cluster level of a data center. Our cooperative technique reduces the power consumption by 65% when compared to traditional techniques (min-min heuristic).
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U2 - 10.1109/IPDPS.2007.370510
DO - 10.1109/IPDPS.2007.370510
M3 - Conference contribution
AN - SCOPUS:34548753308
SN - 1424409101
SN - 9781424409105
T3 - Proceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM
BT - Proceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM
T2 - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007
Y2 - 26 March 2007 through 30 March 2007
ER -