Cluster Computing

, Volume 16, Issue 1, pp 3–15 | Cite as

An overview of energy efficiency techniques in cluster computing systems

  • Giorgio Luigi Valentini
  • Walter Lassonde
  • Samee Ullah KhanEmail author
  • Nasro Min-Allah
  • Sajjad A. Madani
  • Juan Li
  • Limin Zhang
  • Lizhe Wang
  • Nasir Ghani
  • Joanna Kolodziej
  • Hongxiang Li
  • Albert Y. Zomaya
  • Cheng-Zhong Xu
  • Pavan Balaji
  • Abhinav Vishnu
  • Fredric Pinel
  • Johnatan E. Pecero
  • Dzmitry Kliazovich
  • Pascal Bouvry


Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced with strategic power management, we focus in this survey on the characteristic of two main power management technologies: (a) static power management (SPM) systems that utilize low-power components to save the energy, and (b) dynamic power management (DPM) systems that utilize software and power-scalable components to optimize the energy consumption. We present the current state of the art in both of the SPM and DPM techniques, citing representative examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.


Cluster computing Energy efficiency Power management Survey 



Complementary Metal-oxide-Semiconductor


Central Processing Unit


CPU Management Infra-Structure for Energy Reduction


Dynamic Frequency Scaling


Dynamic Power Management


Dynamic Voltage Scaling


Dynamic Voltage and Frequency Scaling


Fast Array of Wimpy Nodes


Grand Challenge Applications


High Availability


High-Performance Computing


Information Technology


Load Balancing

Memory MISER

Memory Management Infra-Structure for Energy Reduction


National Aeronautics and Space Administration


NASA Advanced Supercomputing


NAS Division Parallel Benchmarks

PART system

Power-aware Run-time System

PID controller

Proportional-Integral-Derivative controller


Power-Scalable Components


Synchronous Dynamic Random Access Memory


Static Power Management


Virtual Local-area Network


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Giorgio Luigi Valentini
    • 1
    • 11
  • Walter Lassonde
    • 1
  • Samee Ullah Khan
    • 1
    Email author
  • Nasro Min-Allah
    • 2
  • Sajjad A. Madani
    • 2
  • Juan Li
    • 1
  • Limin Zhang
    • 1
  • Lizhe Wang
    • 3
  • Nasir Ghani
    • 4
  • Joanna Kolodziej
    • 5
  • Hongxiang Li
    • 6
  • Albert Y. Zomaya
    • 7
  • Cheng-Zhong Xu
    • 8
  • Pavan Balaji
    • 9
  • Abhinav Vishnu
    • 10
  • Fredric Pinel
    • 11
  • Johnatan E. Pecero
    • 11
  • Dzmitry Kliazovich
    • 11
  • Pascal Bouvry
    • 11
  1. 1.NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer EngineeringNorth Dakota State UniversityFargoUSA
  2. 2.COMSATS Institute of Information TechnologyIslamabadPakistan
  3. 3.Indiana UniversityBloomingtonUSA
  4. 4.University of New MexicoAlbuquerqueUSA
  5. 5.University of Bielsko-BialaBielsko-BialaPoland
  6. 6.University of LouisvilleLouisvilleUSA
  7. 7.University of SydneySydneyAustralia
  8. 8.Wayne State UniversityDetroitUSA
  9. 9.Argonne National LaboratoryArgonneUSA
  10. 10.Pacific Northwest National LaboratoryRichlandUSA
  11. 11.University of LuxembourgLuxembourgLuxembourg

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