Autonomic power and performance management for computing systems |
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Authors: | Bithika Khargharia Salim Hariri Mazin S Yousif |
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Institution: | (1) University of Arizona, Tucson, AZ, USA;(2) Intel Corporation, Hillsboro, OR, USA |
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Abstract: | With the increased complexity of platforms, the growing demand of applications and data centers’ servers sprawl, power consumption
is reaching unsustainable limits. The need to improved power management is becoming essential for many reasons including reduced
power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents
a theoretical framework and methodology for autonomic power and performance management in e-business data centers. We optimize
for power and performance (performance-per-watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous
optimization approach to minimize power while meeting performance constraints. Our experimental results show around 72% savings
in power while maintaining performance as compared to static power management techniques and 69.8% additional savings with
both global and local optimizations.
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Keywords: | Autonomic management Power Optimization |
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