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    Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks

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    Author(s)
    Ho Lee, Moon
    Hashem Ali Khan, Md.
    Language
    English
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    Abstract
    This chapter provides an introduction to quantifying the energy consumed by software. It is written for computer scientists, software engineers, embedded system developers and programmers who want to understand how to measure the energy consumed by the code they write in order to optimize for energy efficiency. We start with an overview of the electrical foundations of energy measurement and show how these are applied by reviewing the most commonly found energy sensing techniques. This is followed by a brief discussion of the signal processing required to obtain energy consumption data from sensing. We then present two energy measurement systems that are based on sensing techniques. Both can be used to directly measure the energy consumed by software running on embedded systems without the need to modify the hardware. As an alternative, regression-based techniques can be used to infer energy consumption based on monitoring events during program execution using counters monitors offered by the hardware. We introduce the foundations of regression analysis and illustrate how an energy model for an ARM processor can be built using linear regression. In the conclusion, we offer a wider discussion on what should be considered when selecting an energy measurement technique.
    URI
    https://library.oapen.org/handle/20.500.12657/49157
    Keywords
    energy measurement, power, energy sensing, energy measurement systems, regression analysis
    DOI
    10.5772/66052
    Publisher
    InTechOpen
    Publisher website
    https://www.intechopen.com/
    Publication date and place
    2016
    Classification
    Sustainability
    Rights
    https://creativecommons.org/licenses/by/3.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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