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Article

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

1
Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
2
Physics Department, Merton College, Merton St, Oxford OX1 4JD, UK
3
Qarnot Computing, 40–42 Rue Barbès, 92120 Montrouge, France
*
Author to whom correspondence should be addressed.
Academic Editor: Geoff Merrett
Sensors 2021, 21(8), 2879; https://doi.org/10.3390/s21082879
Received: 26 March 2021 / Revised: 16 April 2021 / Accepted: 16 April 2021 / Published: 20 April 2021
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. View Full-Text
Keywords: heat reuse; distributed data centers; workload scheduling; machine learning; mathematical modeling heat reuse; distributed data centers; workload scheduling; machine learning; mathematical modeling
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MDPI and ACS Style

Antal, M.; Cristea, A.-A.; Pădurean, V.-A.; Cioara, T.; Anghel, I.; Antal, C.; Salomie, I.; Saintherant, N. Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers. Sensors 2021, 21, 2879. https://doi.org/10.3390/s21082879

AMA Style

Antal M, Cristea A-A, Pădurean V-A, Cioara T, Anghel I, Antal C, Salomie I, Saintherant N. Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers. Sensors. 2021; 21(8):2879. https://doi.org/10.3390/s21082879

Chicago/Turabian Style

Antal, Marcel, Andrei-Alexandru Cristea, Victor-Alexandru Pădurean, Tudor Cioara, Ionut Anghel, Claudia Antal, Ioan Salomie, and Nicolas Saintherant. 2021. "Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers" Sensors 21, no. 8: 2879. https://doi.org/10.3390/s21082879

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