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An overview of energy efficiency techniques in cluster computing systems

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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.

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Abbreviations

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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Authors and Affiliations

  1. NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, 58108-6050, USA

    Giorgio Luigi Valentini, Walter Lassonde, Samee Ullah Khan, Juan Li & Limin Zhang

  2. COMSATS Institute of Information Technology, Islamabad, Pakistan

    Nasro Min-Allah & Sajjad A. Madani

  3. Indiana University, Bloomington, IN, USA

    Lizhe Wang

  4. University of New Mexico, Albuquerque, NM, USA

    Nasir Ghani

  5. University of Bielsko-Biala, 43300, Bielsko-Biala, Poland

    Joanna Kolodziej

  6. University of Louisville, Louisville, KY, USA

    Hongxiang Li

  7. University of Sydney, Sydney, NSW, 2006, Australia

    Albert Y. Zomaya

  8. Wayne State University, Detroit, MI, USA

    Cheng-Zhong Xu

  9. Argonne National Laboratory, Argonne, IL, USA

    Pavan Balaji

  10. Pacific Northwest National Laboratory, Richland, WA, USA

    Abhinav Vishnu

  11. University of Luxembourg, Luxembourg, L1359, Luxembourg

    Giorgio Luigi Valentini, Fredric Pinel, Johnatan E. Pecero, Dzmitry Kliazovich & Pascal Bouvry

Authors
  1. Giorgio Luigi Valentini
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  2. Walter Lassonde
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  6. Juan Li
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  7. Limin Zhang
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  8. Lizhe Wang
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  10. Joanna Kolodziej
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  11. Hongxiang Li
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  12. Albert Y. Zomaya
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  13. Cheng-Zhong Xu
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  14. Pavan Balaji
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  15. Abhinav Vishnu
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  16. Fredric Pinel
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  19. Pascal Bouvry
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Correspondence to Samee Ullah Khan.

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Valentini, G.L., Lassonde, W., Khan, S.U. et al. An overview of energy efficiency techniques in cluster computing systems. Cluster Comput 16, 3–15 (2013). https://doi.org/10.1007/s10586-011-0171-x

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