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Autonomic power and performance management of high-performance servers

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6 Author(s)
Bithika Khargharia ; NSF Center for Autonomic Computing, University of Arizona, Tucson, USA ; Salim Hariri ; Wael Kdouh ; Manal Houri
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With the increased complexity of platforms coupled with data centers' servers sprawl, power consumption is reaching unsustainable limits. Researchers have addressed data centers' power & performance management at different hierarchies going from server clusters to servers to individual components within the server. This paper presents a novel technique for autonomic power & performance management of a high-performance server platform that consists of multi-core processor and multi-rank memory subsystems. Both the processor and/or the memory subsystem are dynamically reconfigured (expanded or contracted) to suit the application resource requirements. The reconfigured platform creates the opportunity for power savings by transitioning any unused platform capacity (processor/memory) into low-power states for as long as the platform performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to platform performance parameters, which is formulated as an optimization problem. Our experimental results show around 58.33% savings in power as compared to static power management techniques.

Published in:

Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on

Date of Conference:

14-18 April 2008