Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cool...

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Autores principales:	Rajendra Kumar, Sunil Kumar Khatri, Mario José Diván
Formato:	article
Lenguaje:	EN
Publicado:	International Journal of Mathematical, Engineering and Management Sciences 2021
Materias:	data data center energy efficiency power losses temperature relative humidity machine learning Technology T Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/7ef6e1ba654241f78709646d3d424c99
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id	oai:doaj.org-article:7ef6e1ba654241f78709646d3d424c99
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spelling	oai:doaj.org-article:7ef6e1ba654241f78709646d3d424c992021-12-04T05:10:29ZPower Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures10.33889/IJMEMS.2021.6.6.0952455-7749https://doaj.org/article/7ef6e1ba654241f78709646d3d424c992021-12-01T00:00:00Zhttps://ijmems.in/cms/storage/app/public/uploads/volumes/95-IJMEMS-21-0170-6-6-1594-1611-2021.pdfhttps://doaj.org/toc/2455-7749The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.Rajendra KumarSunil Kumar KhatriMario José DivánInternational Journal of Mathematical, Engineering and Management Sciencesarticledatadata centerenergy efficiencypower lossestemperaturerelative humiditymachine learningTechnologyTMathematicsQA1-939ENInternational Journal of Mathematical, Engineering and Management Sciences, Vol 6, Iss 6, Pp 1594-1611 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data data center energy efficiency power losses temperature relative humidity machine learning Technology T Mathematics QA1-939
spellingShingle	data data center energy efficiency power losses temperature relative humidity machine learning Technology T Mathematics QA1-939 Rajendra Kumar Sunil Kumar Khatri Mario José Diván Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
description	The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.
format	article
author	Rajendra Kumar Sunil Kumar Khatri Mario José Diván
author_facet	Rajendra Kumar Sunil Kumar Khatri Mario José Diván
author_sort	Rajendra Kumar
title	Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
title_short	Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
title_full	Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
title_fullStr	Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
title_full_unstemmed	Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures
title_sort	power usage efficiency (pue) optimization with counterpointing machine learning techniques for data center temperatures
publisher	International Journal of Mathematical, Engineering and Management Sciences
publishDate	2021
url	https://doaj.org/article/7ef6e1ba654241f78709646d3d424c99
work_keys_str_mv	AT rajendrakumar powerusageefficiencypueoptimizationwithcounterpointingmachinelearningtechniquesfordatacentertemperatures AT sunilkumarkhatri powerusageefficiencypueoptimizationwithcounterpointingmachinelearningtechniquesfordatacentertemperatures AT mariojosedivan powerusageefficiencypueoptimizationwithcounterpointingmachinelearningtechniquesfordatacentertemperatures
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Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

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