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An Empirical Study of Power Characterization Approaches for Servers

Abstract : Data centers are energy-hungry facilities. Emerging studies have proposed energy-aware solutions in reducing power consumption of Data center. Power consumption characterization of servers is an essential part to realize power-aware adaption strategies. Traditional methods adopt accuracy and secures direct measurements by using physical instruments such as wattmeters. Recently, devices-less solutions have drawn much attention as an economical replacement. Device-less solutions provide power consumption information a computing platform by using self resources without additional physical instruments. There are two common adopted devices-less solutions: 1) standard specifications that provide interface with integrated sensors, such as IPMI (Intelligent Platform Management Interface) and Redfish; 2) Power models based on system activity related indicators. The energy-aware scheduling decisions are made based on the power values obtained, but few works give information about the correctness of the power values while discussing the results or drawing conclusions. In this study, we try to fill up this missing part by evaluating some common used, economical manners in obtaining power values. We compare and discuss the reliability, advantages and limitations for the CPU-utilization based power models. The findings highlight the challenges in realizing accurate and reliable power models. we also evaluate the reliability of IPMI and RedFish as well, in order to give references in choosing appropriate power characterization solutions.
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Contributor : Jean-Marc Menaud <>
Submitted on : Monday, May 6, 2019 - 8:22:31 AM
Last modification on : Tuesday, January 5, 2021 - 4:26:24 PM


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  • HAL Id : hal-02120589, version 1


Yewan Wang, David Nörtershäuser, Stéphane Le Masson, Jean-Marc Menaud. An Empirical Study of Power Characterization Approaches for Servers. ENERGY 2019 - The Ninth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, Jun 2019, Athens, Greece. pp.1-6. ⟨hal-02120589v1⟩



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