An overview of energy efficiency techniques in cluster computing systems

  • Authors:

  • 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

  • Affiliations:

  • NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 58108-6050 and University of Luxembourg, Luxem ...;NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 58108-6050;NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 58108-6050;COMSATS Institute of Information Technology, Islamabad, Pakistan;COMSATS Institute of Information Technology, Islamabad, Pakistan;NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 58108-6050;NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 58108-6050;Indiana University, Bloomington, USA;University of New Mexico, Albuquerque, USA;University of Bielsko-Biala, Bielsko-Biala, Poland 43300;University of Louisville, Louisville, USA;University of Sydney, Sydney, Australia 2006;Wayne State University, Detroit, USA;Argonne National Laboratory, Argonne, USA;Pacific Northwest National Laboratory, Richland, USA;University of Luxembourg, Luxembourg, Luxembourg L1359;University of Luxembourg, Luxembourg, Luxembourg L1359;University of Luxembourg, Luxembourg, Luxembourg L1359;University of Luxembourg, Luxembourg, Luxembourg L1359

  • Venue:
  • Cluster Computing
  • Year:
  • 2013

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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.