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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2021-12-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://ijmems.in/cms/storage/app/public/uploads/volumes/95-IJMEMS-21-0170-6-6-1594-1611-2021.pdf |
| _version_ | 1818343715083649024 |
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| author | Rajendra Kumar Sunil Kumar Khatri Mario José Diván |
| author_facet | Rajendra Kumar Sunil Kumar Khatri Mario José Diván |
| author_sort | Rajendra Kumar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T16:34:59Z |
| format | Article |
| id | doaj.art-7ef6e1ba654241f78709646d3d424c99 |
| institution | Directory Open Access Journal |
| issn | 2455-7749 |
| language | English |
| last_indexed | 2024-12-13T16:34:59Z |
| publishDate | 2021-12-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-7ef6e1ba654241f78709646d3d424c992022-12-21T23:38:25ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492021-12-01661594161110.33889/IJMEMS.2021.6.6.095Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center TemperaturesRajendra Kumar0Sunil Kumar Khatri1Mario José Diván2Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.Amity University Tashkent, Uzbekistan.Data Science Research Group, Economy School, National University of La Pampa Santa Rosa, La Pampa, Argentina.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.https://ijmems.in/cms/storage/app/public/uploads/volumes/95-IJMEMS-21-0170-6-6-1594-1611-2021.pdfdatadata centerenergy efficiencypower lossestemperaturerelative humiditymachine learning |
| spellingShingle | Rajendra Kumar Sunil Kumar Khatri Mario José Diván Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures International Journal of Mathematical, Engineering and Management Sciences data data center energy efficiency power losses temperature relative humidity machine learning |
| title | 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_short | 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 |
| topic | data data center energy efficiency power losses temperature relative humidity machine learning |
| url | https://ijmems.in/cms/storage/app/public/uploads/volumes/95-IJMEMS-21-0170-6-6-1594-1611-2021.pdf |
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