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Issue No.01 - January (2012 vol.23)
pp: 168-176
Xiaorui Wang , Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA
In today's data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are not scalable to control the power consumption of an entire large-scale data center, because these solutions are designed only for a single server or a rack enclosure. In a modern data center, however, power control needs to be enforced at three levels: rack enclosure, power distribution unit, and the entire data center, due to the physical and contractual power limits at each level. This paper presents SHIP, a highly scalable hierarchical power control architecture for large-scale data centers. SHIP is designed based on well-established control theory for analytical assurance of control accuracy and system stability. Empirical results on a physical testbed show that our control solution can provide precise power control, as well as power differentiations for optimized system performance and desired server priorities. In addition, our extensive simulation results based on a real trace file demonstrate the efficacy of our control solution in large-scale data centers composed of 5,415 servers.
power control, computer centres, network servers, power aware computing, power consumption, system stability, SHIP, scalable hierarchical power control architecture, data center, server power consumption control, system failure avoidance, power capacity overload, overheating, rack enclosure, power distribution unit, Servers, Power demand, Power control, Frequency modulation, Circuit breakers, Frequency control, Monitoring, servers., Power capping, data centers, control theory, power management, scalability
Xiaorui Wang, "SHIP: A Scalable Hierarchical Power Control Architecture for Large-Scale Data Centers", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 1, pp. 168-176, January 2012, doi:10.1109/TPDS.2011.93
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