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      The impact of location variables on the modelling and forecasting of capital expenditure for hyperscale datacenters

        , 1

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      Data Center, Cost Planning, Cost Modelling, CAPEX, Capital Expenditure

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          The digital age and the growth of the internet is increasing exponentially each year, this has created a need for facilities to house and store the data generated. Examples of the data generated are such items such as images, film libraries such as Netflix and Prime Video along withinternet search data.

          The facility to house this information is a data center. A data centeris a building (or self-contained unit within a building) used to house computing equipment such as servers along with associated components such as telecommunications, network and storage systems.

          This growth in demand has required data centerproviders to expand into new countries, often at very short notice.

          Research Design

          The research design will likely use a positivist paradigm with a deductive approach, combining predictive theory and action research theory using a multivariate linear regression technique.

          The Data will be collected using closed questionnaires, publicly available data and industry data analysed in a quantitative method with a cross sectional timeline.

          Research Findings

          Variables will be established and analysed to understand the weighting of each variable and its impact on capital expenditure.

          Research Conclusions

          The conclusion is expected to be a model where input location variables are used to predict the capital expenditure for the construction of hyperscale data centers.

          This will benefit data centerowners, developers and fund providers when assessing the value of capital expenditure required as a decision for investment in selecting a site location fora hyperscale data center.

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          Author and article information

          ScienceOpen Posters
          22 January 2021
          [1 ] Anglia Ruskin University, Faculty of Science and Engineering (School of Engineering and the Built Environment), Bishop Hall Lane, Chelmsford, CM1 1SQ, United Kingdom

          This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

          Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

          Computer science, Statistics, Engineering

          Data Center, Cost Planning, Capital Expenditure, Cost Modelling, CAPEX


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