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Information 2019, 10(3), 113; https://doi.org/10.3390/info10030113

An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

1,*,†
,
2,†
,
3,†
,
4,†
and
1,†
1
Informatics Center, Cidade Universitária, Federal University of Pernambuco, Recife 50740-560, Brazil
2
Department of Computing, Federal Rural University of Pernambuco, Dom Manuel de Medeiros, Dois Irmãos, Recife 52171-900, Brazil
3
Department of Informatics, Federal University of Amazonas, Manaus 69020-120, Brazil
4
Automation Technologye, Bergische Universität Wuppertal, D-42119 Wuppertal, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 24 December 2018 / Revised: 4 March 2019 / Accepted: 5 March 2019 / Published: 14 March 2019
(This article belongs to the Special Issue Fault Diagnosis, Maintenance and Reliability)
Full-Text   |   PDF [1432 KB, uploaded 14 March 2019]   |  

Abstract

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. View Full-Text
Keywords: energy flow model; sustainability; data center power architectures; optimization; multilayer perceptron energy flow model; sustainability; data center power architectures; optimization; multilayer perceptron
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Ferreira, J.; Callou, G.; Josua, A.; Tutsch, D.; Maciel, P. An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers. Information 2019, 10, 113.

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