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Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers
Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073740/ https://www.ncbi.nlm.nih.gov/pubmed/33924008 http://dx.doi.org/10.3390/s21082879 |
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author | Antal, Marcel Cristea, Andrei-Alexandru Pădurean, Victor-Alexandru Cioara, Tudor Anghel, Ionut Antal (Pop), Claudia Salomie, Ioan Saintherant, Nicolas |
author_facet | Antal, Marcel Cristea, Andrei-Alexandru Pădurean, Victor-Alexandru Cioara, Tudor Anghel, Ionut Antal (Pop), Claudia Salomie, Ioan Saintherant, Nicolas |
author_sort | Antal, Marcel |
collection | PubMed |
description | Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand. |
format | Online Article Text |
id | pubmed-8073740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80737402021-04-27 Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers Antal, Marcel Cristea, Andrei-Alexandru Pădurean, Victor-Alexandru Cioara, Tudor Anghel, Ionut Antal (Pop), Claudia Salomie, Ioan Saintherant, Nicolas Sensors (Basel) Article Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand. MDPI 2021-04-20 /pmc/articles/PMC8073740/ /pubmed/33924008 http://dx.doi.org/10.3390/s21082879 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Antal, Marcel Cristea, Andrei-Alexandru Pădurean, Victor-Alexandru Cioara, Tudor Anghel, Ionut Antal (Pop), Claudia Salomie, Ioan Saintherant, Nicolas Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title | Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title_full | Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title_fullStr | Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title_full_unstemmed | Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title_short | Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers |
title_sort | heating homes with servers: workload scheduling for heat reuse in distributed data centers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073740/ https://www.ncbi.nlm.nih.gov/pubmed/33924008 http://dx.doi.org/10.3390/s21082879 |
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