Autonomic power and performance management for computing systems

Bithika Khargharia, Salim A Hariri, Mazin S. Yousif

Research output: Contribution to journalArticle

61 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)167-181
Number of pages15
JournalCluster Computing
Volume11
Issue number2
DOIs
StatePublished - Jun 2008

Fingerprint

Electric power utilization
Scalability
Servers
Cooling
Industry
Power management
Compliance

Keywords

  • Autonomic management
  • Optimization
  • Power

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Autonomic power and performance management for computing systems. / Khargharia, Bithika; Hariri, Salim A; Yousif, Mazin S.

In: Cluster Computing, Vol. 11, No. 2, 06.2008, p. 167-181.

Research output: Contribution to journalArticle

Khargharia, Bithika ; Hariri, Salim A ; Yousif, Mazin S. / Autonomic power and performance management for computing systems. In: Cluster Computing. 2008 ; Vol. 11, No. 2. pp. 167-181.
@article{3c7c18da3792450bac21900e37d2683c,
title = "Autonomic power and performance management for computing systems",
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.",
keywords = "Autonomic management, Optimization, Power",
author = "Bithika Khargharia and Hariri, {Salim A} and Yousif, {Mazin S.}",
year = "2008",
month = "6",
doi = "10.1007/s10586-007-0043-6",
language = "English (US)",
volume = "11",
pages = "167--181",
journal = "Cluster Computing",
issn = "1386-7857",
publisher = "Kluwer Academic Publishers",
number = "2",

}

TY - JOUR

T1 - Autonomic power and performance management for computing systems

AU - Khargharia, Bithika

AU - Hariri, Salim A

AU - Yousif, Mazin S.

PY - 2008/6

Y1 - 2008/6

N2 - 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.

AB - 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.

KW - Autonomic management

KW - Optimization

KW - Power

UR - http://www.scopus.com/inward/record.url?scp=40949162877&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40949162877&partnerID=8YFLogxK

U2 - 10.1007/s10586-007-0043-6

DO - 10.1007/s10586-007-0043-6

M3 - Article

AN - SCOPUS:40949162877

VL - 11

SP - 167

EP - 181

JO - Cluster Computing

JF - Cluster Computing

SN - 1386-7857

IS - 2

ER -