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Autonomic power and performance management for computing systems

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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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Authors and Affiliations

  1. University of Arizona, Tucson, AZ, USA

    Bithika Khargharia & Salim Hariri

  2. Intel Corporation, Hillsboro, OR, USA

    Mazin S. Yousif

Authors
  1. Bithika Khargharia
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  2. Salim Hariri
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  3. Mazin S. Yousif
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Correspondence to Bithika Khargharia.

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Khargharia, B., Hariri, S. & Yousif, M.S. Autonomic power and performance management for computing systems. Cluster Comput 11, 167–181 (2008). https://doi.org/10.1007/s10586-007-0043-6

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  • DOI: https://doi.org/10.1007/s10586-007-0043-6

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