IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5222-d424559.html
   My bibliography  Save this article

A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms

Author

Listed:
  • Kosuke Sasakura

    (NTT FACILITIES INC, Kotoku, Tokyo 135–0007, Japan)

  • Takeshi Aoki

    (NTT FACILITIES INC, Kotoku, Tokyo 135–0007, Japan)

  • Masayoshi Komatsu

    (NTT FACILITIES INC, Kotoku, Tokyo 135–0007, Japan)

  • Takeshi Watanabe

    (NTT FACILITIES INC, Kotoku, Tokyo 135–0007, Japan)

Abstract

As data centers have become increasingly important in recent years their operational management must attain higher efficiency and reliability. Moreover, the power consumption of a data center is extremely large, and it is anticipated that it will continue to increase, so energy saving has become an urgent issue concerning data centers. In the meantime, the environment of the server rooms in data centers has become complicated owing to the introduction of virtualization technology, the installation of high-heat density information and communication technology (ICT) equipment and racks, and the diversification of cooling methods. It is very difficult to manage a server room in the case of such a complicated environment. When energy-saving measures are implemented in a server room with such a complicated environment, it is important to evaluate “temperature risks” in advance and calculate the energy-saving effect after the measures are taken. Under those circumstances, in this study, two prediction models are proposed: a model that predicts the rack intake temperature (so that the temperature risk can be evaluated in support of energy-saving measures implemented in the server room) and a model that evaluates the energy-saving effect (in relation to a baseline). Specifically, the models were constructed by using machine learning. The first constructed model evaluates the temperature risk in a verification room in advance, and it was confirmed that the model can evaluate the risk beforehand with high accuracy. The second constructed model—“baseline model” hereafter—supports energy-saving measures, and it was confirmed that the model can calculate the baseline (energy consumption) with high accuracy as well. Moreover, the effect of proposal process of energy-saving measures in the verification room was verified by using the two proposed models. In particular, the effectiveness of the model for evaluating temperature risk in advance and that of a technology for visualizing the energy-saving effect were confirmed.

Suggested Citation

  • Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms," Energies, MDPI, vol. 13(19), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5222-:d:424559
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5222/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/19/5222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2019. "Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD," Energies, MDPI, vol. 12(8), pages 1-17, April.
    2. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    3. Anders S. G. Andrae & Tomas Edler, 2015. "On Global Electricity Usage of Communication Technology: Trends to 2030," Challenges, MDPI, vol. 6(1), pages 1-41, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room," Energies, MDPI, vol. 13(17), pages 1-17, August.
    2. Steffen Dalsgaard, 2022. "Can IT Resolve the Climate Crisis? Sketching the Role of an Anthropology of Digital Technology," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    3. Axenbeck, Janna & Niebel, Thomas, 2021. "Climate Protection Potentials of Digitalized Production Processes: Microeconometric Evidence," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238007, International Telecommunications Society (ITS).
    4. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    5. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    6. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    7. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    8. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    9. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
    10. Babasola Osibo & Simisola Adamo, 2023. "Data Centers and Green Energy: Paving the Way for a Sustainable Digital Future," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 12(11), pages 15-30, November.
    11. Li Wei & Ming-Chih Lee & Wan-Hsiu Cheng & Chia-Hsien Tang & Jing-Wun You, 2023. "Evaluating the Efficiency of Financial Assets as Hedges against Bitcoin Risk during the COVID-19 Pandemic," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    12. Anders S. G. Andrae & Mengjun Xia & Jianli Zhang & Xiaoming Tang, 2016. "Practical Eco-Design and Eco-Innovation of Consumer Electronics—the Case of Mobile Phones," Challenges, MDPI, vol. 7(1), pages 1-19, February.
    13. Jing Tian & Jan P.A.M. Jacobs & Denise R. Osborn, 2024. "Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1260-1289, October.
    14. Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2019. "A Comparative Study of Methods for Measurement of Energy of Computing," Energies, MDPI, vol. 12(11), pages 1-42, June.
    15. Tilman Santarius & Johanna Pohl & Steffen Lange, 2020. "Digitalization and the Decoupling Debate: Can ICT Help to Reduce Environmental Impacts While the Economy Keeps Growing?," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    16. Philipp Adämmer & Martin T. Bohl, 2018. "Price discovery dynamics in European agricultural markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(5), pages 549-562, May.
    17. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.
    18. Michael ARTIS & Massimiliano MARCELLINO & Tommaso PROIETTI, 2002. "Dating the Euro Area Business Cycle," Economics Working Papers ECO2002/24, European University Institute.
    19. Anders S. G. Andrae & Mikko Samuli Vaija, 2017. "Precision of a Streamlined Life Cycle Assessment Approach Used in Eco-Rating of Mobile Phones," Challenges, MDPI, vol. 8(2), pages 1-24, August.
    20. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5222-:d:424559. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.