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.
Highlights
More people than ever have access to the Internet
This paper proposes an extension to the energy flow model (EFM) that may consider different energy sources in the system under analysis
We estimated CO2 emissions and data center energy consumption considering the variation of the generating source, as well as forecasting the energy consumption over the months
Summary
More people than ever have access to the Internet. Social changes have transformed both the way people live and how the world works. The main focus of this paper is to propose an integrated strategy to evaluate the operational costs and estimate the environmental impacts (CO2 emissions) of data centers In this strategy, an artificial neural network (ANN) is applied to the energy flow model (EFM). The main contributions of this work are as follows: considering the energy mix of the data centers to estimate the emission of carbon dioxide in the atmosphere through the energy consumption of the centers mentioned above; cost evaluation, availability and sustainability for the electric infrastructures of the data centers; and use of an artificial neural network (ANN) along with the energy flow model (EFM), to predict the consumption of energy in the few months, based on the environment history.
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