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 Khan
  • 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
Article

Abstract

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.

Keywords

Cluster computing Energy efficiency Power management Survey 

Acronyms

CMOS

Complementary Metal-oxide-Semiconductor

CPU

Central Processing Unit

CPU MISER

CPU Management Infra-Structure for Energy Reduction

DFS

Dynamic Frequency Scaling

DPM

Dynamic Power Management

DVS

Dynamic Voltage Scaling

DVFS

Dynamic Voltage and Frequency Scaling

FAWN

Fast Array of Wimpy Nodes

GCA

Grand Challenge Applications

HA

High Availability

HPC

High-Performance Computing

IT

Information Technology

LB

Load Balancing

Memory MISER

Memory Management Infra-Structure for Energy Reduction

NASA

National Aeronautics and Space Administration

NAS

NASA Advanced Supercomputing

NPB

NAS Division Parallel Benchmarks

PART system

Power-aware Run-time System

PID controller

Proportional-Integral-Derivative controller

PSC

Power-Scalable Components

SDRAM

Synchronous Dynamic Random Access Memory

SPM

Static Power Management

VLAN

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
  • 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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