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Descripción
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| In the data center?s scope, current cooling techniques are not very efficient both in terms of energy, consuming up to 40% of the total energy requirements, and in terms of occupied area. This is a critical problem for the development of new smart cities, which require the proliferation of numerous data centers in urban areas, to reduce latency and bandwidth of processing data analytics applications in real time. In this work, we propose a new disruptive solution developed to address this problem, submerging the computing infrastructure in a tank full of a dielectric liquid based on hydro-fluoro-ethers (HFE). Thus, we obtain a passive two phase-cooling system, achieving zero-energy cooling and reducing its area. However, to ensure the maximum heat transfer capacity of the HFE, it is necessary to ensure specific thermal conditions. Making a predictive model is crucial for any system that needs to work around the point of maximum efficiency. Therefore, this research focuses on the implementation of a predictive thermal model, accurate enough to keep the temperature of the cooling system within the maximum efficiency region, under real workload conditions. In this paper, we successfully obtained a predictive thermal model using a neural network architecture based on a Gated Recurrent Unit. This model makes accurate thermal predictions of a real system based on HFE immersion cooling, presenting an average error of 0.75 degC with a prediction window of 1 min. | |
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Internacional
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Si |
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Nombre congreso
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International Conference on Intelligent Data Engineering and Automated Learning |
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Tipo de participación
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960 |
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Lugar del congreso
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Madrid |
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Revisores
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Si |
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ISBN o ISSN
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978-3-030-03492-4 |
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DOI
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https://doi.org/10.1007/978-3-030-03493-1_51 |
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Fecha inicio congreso
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21/11/2018 |
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Fecha fin congreso
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23/11/2018 |
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Desde la página
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491 |
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Hasta la página
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498 |
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Título de las actas
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Intelligent Data Engineering and Automated Learning ? IDEAL 2018 |