Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks
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...
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| Format: | Online |
| Jezik: | angleščina |
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InTechOpen
2021
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| Online dostop: | ONIX_20210602_10.5772/66052_271 |
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| _version_ | 1869515893719957504 |
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| author | Ho Lee, Moon Hashem Ali Khan, Md. |
| author_browse | Hashem Ali Khan, Md. Ho Lee, Moon |
| author_facet | Ho Lee, Moon Hashem Ali Khan, Md. |
| author_sort | Ho Lee, Moon |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-70202 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-702022024-04-09T11:41:20Z Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks Ho Lee, Moon Hashem Ali Khan, Md. energy measurement, power, energy sensing, energy measurement systems, regression analysis thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RN The environment::RNU Sustainability 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. 2021-02-10T12:58:18Z 2021-06-02T10:08:02Z 2016 chapter ONIX_20210602_10.5772/66052_271 https://library.oapen.org/handle/20.500.12657/49157 https://directory.doabooks.org/handle/20.500.12854/70202 eng open access image/jpeg image/jpeg n/a n/a https://library.oapen.org/bitstream/20.500.12657/49157/1/52922.pdf https://library.oapen.org/bitstream/20.500.12657/49157/1/52922.pdf InTechOpen 10.5772/66052 10.5772/66052 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | energy measurement, power, energy sensing, energy measurement systems, regression analysis thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RN The environment::RNU Sustainability Ho Lee, Moon Hashem Ali Khan, Md. Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title | Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title_full | Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title_fullStr | Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title_full_unstemmed | Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title_short | Chapter Energy Efficiency for 5G Multi-Tier Cellular Networks |
| title_sort | chapter energy efficiency for 5g multi tier cellular networks |
| topic | energy measurement, power, energy sensing, energy measurement systems, regression analysis thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RN The environment::RNU Sustainability |
| topic_facet | energy measurement, power, energy sensing, energy measurement systems, regression analysis thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RN The environment::RNU Sustainability |
| url | ONIX_20210602_10.5772/66052_271 |
| work_keys_str_mv | AT holeemoon chapterenergyefficiencyfor5gmultitiercellularnetworks AT hashemalikhanmd chapterenergyefficiencyfor5gmultitiercellularnetworks |