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Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept

  • David King
  • Nadeeshani Wanigarathna
  • Keith Jones
  • Joseph Ofori-Kuragu

The theme of ’The Impact of Engineering Practices on a Sustainable Built Environment’ emphasises the importance of considering various dimensions of resilient infrastructure. Selecting the location for a Hyperscale Data Centre is a crucial process that involves assessing the impact of various location variables. To determine the viability of a location, it is essential to identify the potential risks associated with each variable. This paper presents a proprietary methodological approach that includes a Delphi study to identify risks, a Likert scoring system to assess prior probabilities, and a Bayesian theory-based decision tree to assess the impact through risk prediction. The paper's contributions are significant, and the proposed methodology makes it possible to predict the risk level of each location variable by identifying the appropriate contingency percentage. The study's findings indicate that the paper's proposed approach is an effective way to mitigate the risks associated with selecting a location for a Hyperscale Data Centre. Embracing this knowledge allows us to align research and practise with the conference’s call to studying the resilience of buildings and infrastructure to natural disasters and climate change, and developing strategies for adaptation and mitigation, ensuring that these practises become integral to shaping the future of Data Centres

  • Keywords:
  • Bayes Theorem,
  • Delphi,
  • Data Centre,
  • Location Variables,
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David King

Anglia Ruskin University, United Kingdom - ORCID: 0000-0002-8026-3796

Nadeeshani Wanigarathna

Anglia Ruskin University, United Kingdom - ORCID: 0000-0001-8889-8019

Keith Jones

Anglia Ruskin University, United Kingdom - ORCID: 0000-0002-8883-9673

Joseph Ofori-Kuragu

Anglia Ruskin University, United Kingdom - ORCID: 0000-0003-2872-9437

  1. J. D. Christensen, J. Therkelsen, I. Georgiev, H. Sand, Data centre opportunities in the Nordics: An analysis of the competitive advantages, 1st Edition, Nordic Council of Ministers, Stockholm, 2018. DOI: 10.6027/TN2018-553
  2. M. Avgerinou, P. Bertoldi, L. Castellazzi, Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency, Energies 10 (10) (2017). DOI: 10.3390/en10101470
  3. RICS, Cost analysis and benchmarking, Tech. rep., Royal Institution of Chartered Surveyors (2011). https://www.isurv.com/downloads/file/3096/archived_cost_analysis_and_benchmarking_%E2%80%93_uk_1st_edition_august_2011%E2%80%93june_2022?restricted=true
  4. AECOM, Spon’s Architects’ and Builders’ Price Book 2018, one hundred Edition, CRC, Abingdon, 2017. DOI: 10.1201/b22288
  5. RICS, BCIS Online (2018). https://bcis.co.uk/?gclid=EAIaIQobChMImqj1Lab_gIVB_tCh3e7g6KEAAYAiAAEgLWQPD_BwE
  6. A. Vonderau, Technologies of imagination: Locating the Cloud in Sweden’s North, Imaginations: Journal of Cross-Cultural Image Studies 8 (2) (2017) 8–21. DOI: 10.17742/image.ld.8.2.2
  7. EC, Purchasing power parities (PPPs), price level indices and real expenditures - Eurostat, issue: November 2 Volume: 2019 (2019). https://ec.europa.eu/eurostat/web/purchasing-power-parities/publications
  8. World Bank, The World Bank Consumer Price Index (2022). URL https://data.worldbank.org/indicator/FP.CPI.TOTL?locations=FR-DE-IT-GB-US
  9. OECD, OECD Purchasing Power Parities (2022). URL https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm
  10. D. King, N. Wanigarathna, K. Jones, J. Ofori-Kuragu, A Delphi Pilot Study to Assess the Impact of Location Factors for Hyperscale Data Centres, in G. Lindahl, S. C. Gottlieb (Eds.), SDGs in Construction Economics and Organization, Springer International Publishing, Cham, 2023, pp. 153–164. DOI: 10.1007/978-3-031-25498-7_11
  11. PMI, Project management body of knowledge (pmbok® guide), Vol. 11, 2001, pp. 7–8. DOI: 10.1201/9781439882856-8
  12. C. Bentley, Prince2: A practical handbook, Routledge, 2012. DOI: 10.4324/9780080497792
  13. N. Clibbens, S. Walters, W. Baird, Delphi research: issues raised by a pilot study, Nurse researcher 19 (2) (2012). DOI: 10.7748/nr2012.01.19.2.37.c8907
  14. T. Bayes, An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S, Philosophical transactions of the Royal Society of London (53) (1763) 370–418. DOI: 10.1098/rstl.1763.0053
  15. J. Pearl, Reverend Bayes on inference engines: A distributed hierarchical approach, in Probabilistic and Causal Inference: The Works of Judea Pearl, 2022, pp. 129–138. DOI: 10.1145/3501714.3501727
  16. R. Wetzels, E.-J. Wagenmakers, A default Bayesian hypothesis test for correlations and partial correlations, Psychonomic Bulletin & review 19 (6) (2012) 1057–1064, publisher: Springer. DOI: 10.3758/s13423-012-0295-x
  17. I. H. Sarker, Machine learning: Algorithms, real-world applications and research directions, SN Computer Science 2 (3) (2021) 1–21, publisher: Springer. DOI: 10.1007/s42979-021-00592-x
  18. I. Ajzen, M. Fishbein, A Bayesian analysis of attribution processes., Psychological Bulletin 82 (2) (1975) 261, publisher: American Psychological Association. DOI: 10.1037/h0076477
  19. M. Turoff, H. A. Linstone, The Delphi method-techniques and applications (2002). DOI: 10.2307/1268751
  20. J. Bijak, Forecasting migration: selected models and methods, in Forecasting International Migration in Europe: A Bayesian View, Springer, 2011, pp. 53–87. DOI: 10.1007/978-90-481-8897-0_4
  21. J. M. Bernardo, Bayesian Statistics. Encyclopedia of Life Support Systems (EOLSS). Probability and Statistics (2003). www.uv.es/~bernardo/BayesStat2.pdf
  22. G. Abel, J. Bijak, A. Findlay, D. McCollum, A. Wisniewski, Forecasting environmental migration to the United Kingdom: an exploration using Bayesian models, Population and Environment 35 (2) (2013) 183–203. DOI: 10.1007/s11111-013-0186-8
  23. R. Likert, A technique for the measurement of attitudes., Archives of psychology (1932). https://legacy.voteview.com/pdf/Likert_1932.pdf
  24. Z. Ni, L. D. Phillips, G. B. Hanna, Exploring Bayesian belief networks using netica®, in Evidence Synthesis in Healthcare: A Practical Handbook for Clinicians, Springer, 2011, pp. 293–318. DOI: 10.1007/978-0-85729-206-3_12
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  • Publication Year: 2023
  • Pages: 398-406

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  • Publication Year: 2023

Chapter Information

Chapter Title

Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept

Authors

David King, Nadeeshani Wanigarathna, Keith Jones, Joseph Ofori-Kuragu

DOI

10.36253/979-12-215-0289-3.39

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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