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As one of the increasingly important alternative property sectors, data centres are a technology-focused property sector that is taking advantage of the growing investment intensity in technology-related infrastructure, against the backdrop of constant innovation and advancement in technology. The purpose of this paper is to assess the preliminary risk-adjusted performance and portfolio diversification benefits of data centre Real Estate Investment Trusts (REITs) in the USA, Australia and Singapore. The strategic implications going forward for data centres as an innovative property sector in the property investment space are also highlighted.

Using monthly total returns, the average annual return, annual risk, risk-adjusted performance and portfolio diversification benefits of data centre REITs in the USA, Australia and Singapore over 2016–2018 are assessed. Optimal asset allocation analysis is performed to investigate the value-added role of data centre REITs in a mixed-asset portfolio.

Data centre REITs delivered strong average annual return performance, outperforming the composite REITs in all three markets. This also sees data centre REITs being riskier than the overall REIT sector due to the non-traditional and maturing status of the data centre property sector. On a risk-adjusted basis, competitive performance was recorded for data centre REITs, with data centre REITs in the USA and Singapore outperforming their respective composite REITs. This performance is also delivered with significant portfolio diversification benefits with the stock market, resulting in data centre REITs contributing to the US mixed-asset portfolios across a diverse risk spectrum.

Institutional investors are now giving increased emphasis to alternative property sectors with better risk-return trade-offs. Improved performance and diversification benefits are achieved by supplementing existing property portfolios with non-traditional property sectors with counter-cyclical risk-return profiles, one of which is the data centre property sector. This sees data centres as an important alternative property sector, having technology-based drivers and being recognised as having a clear path towards institutionalisation with the major investors in the near future.

This paper is the first published empirical research analysis that specifically assessed the preliminary performance and diversification benefits of data centre REITs in the USA, Australia and Singapore. This research enables empirically validated, more informed and practical property investment decision making by institutional investors regarding the future strategic role of the data centre property sector as an innovative sector in the institutional property investment space.

The continuing growth of digital technology has been accompanied by an explosion in digital data generation. These data are processed, stored, managed and exchanged in data centres but there is limited general awareness appreciation of the role such centres play as the driving hub of the economy. With this in mind, the purpose of this paper is to provide a simple review of the data centre property market and the changing geography of such centres and to review a number of the environmental and planning issues associated with the development of new data centres.

The paper begins with a description of the characteristics and origins of data centres, outlines the data centre property market and the changing geography of new purpose built data centres within the UK and discusses a range of the environmental and planning issues associated with the development of such centres.

The paper reveals that four different types of data centre can be recognised, that relationship between supply and demand within the data centre property market is a contested issue and that the geography of new data centre development is changing. Data centre developers are increasingly looking for designs that increase energy efficiency and reduce carbon dioxide and the planning issues associated with such developments include classifying the use class, environmental and design considerations and employment generation.

The paper provides an accessible review of the development of data centres in the UK and as such it will be of interest to those working in, engaging with, and studying the data centre market.

The paper provides an accessible review of the development of data centres in the UK and as such it will be of interest to those working in, engaging with, and studying data centres.

The purpose of this paper is to review the available reduced order modeling approaches in the literature for predicting the flow and specially temperature fields inside data centers in terms of the involved design parameters.

This paper begins with a motivation for flow/thermal modeling needs for designing an energy‐efficient thermal management system in data centers. Recent studies on air velocity and temperature field simulations in data centers through computational fluid dynamics/heat transfer (CFD/HT) are reviewed. Meta‐modeling and reduced order modeling are tools to generate accurate and rapid surrogate models for a complex system. These tools, with a focus on low‐dimensional models of turbulent flows are reviewed. Reduced order modeling techniques based on turbulent coherent structures identification, in particular the proper orthogonal decomposition (POD) are explained and reviewed in more details. Then, the available approaches for rapid thermal modeling of data centers are reviewed. Finally, recent studies on generating POD‐based reduced order thermal models of data centers are reviewed and representative results are presented and compared for a case study.

It is concluded that low‐dimensional models are needed in order to predict the multi‐parameter dependent thermal behavior of data centers accurately and rapidly for design and control purposes. POD‐based techniques have shown great approximation for multi‐parameter thermal modeling of data centers. It is believed that wavelet‐based techniques due to the their ability to separate between coherent and incoherent structures – something that POD cannot do – can be considered as new promising tools for reduced order thermal modeling of complex electronic systems such as data centers

The paper reviews different numerical methods and provides the reader with some insight for reduced order thermal modeling of complex convective systems such as data centers.

The purpose of this paper is to identify energy efficiency metrics that can be used by IT managers to measure and maintain the implementation of cost savings and green initiatives in data centers.

The paper looks at the background of the problem and explores the reasons why energy savings in the data center are an important issue. Included are interviews and survey results from IT professionals serving at four unique organizations. A model of the measurable components of a data center is created to provide a framework for organizing metrics and communicating results throughout the corporation. The strengths and weaknesses of two of the most common data center metrics, PUE and DCP, are examined closely.

The paper concludes with future metric recommendations and a proposed credit‐based system that could be applied to encourage closer management of these metrics.

The metric recommendations can be used by IT managers resulting in energy efficiency improvements in their data centers.

The paper provides a good comprehension of multiple approaches and makes recommendations for a platform metric that can be further developed and adopted as a standard.

This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied.

Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots.

Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center.

The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized.

The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption.

This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

In 1986 The Roper Center celebrated its fortieth year as the largest archive of survey data in the world. It was, in fact, 1946 when Elmo Roper founded the center at Williams College. A collection of 177 surveys conducted by Roper Research Associates served to establish the center as the first archive of polling data. From the start, the center laid the framework by which other pioneers in the field of opinion research, especially George Gallup, Sr., came to appreciate its scholarly nature. Subsequently many of these pioneers helped increase its holdings by donating survey material of their own.

The purpose of this practice briefing paper is to provide an exploratory review of the sustainability agendas being addressed and publicly reported by the UK's leading data centre operators.

The paper begins with a short discussion of the characteristics of sustainability and an outline of the origins and development of data centres within the UK. The paper draws its empirical material from the most recent information on sustainability posted on the UK's leading data centre operators' corporate web sites.

The findings reveal that all the UK's leading data centre operators provide only limited information on their commitment to sustainability with the dominant focus being on its environmental dimension and with little attention being paid to social and economic issues. More critically the authors argue that these commitments are driven more by the search for efficiency gains, that they are couched within existing business models centred on continuing growth and that as such the UK's leading data centre operators are pursuing a “weak” rather than a “strong” model of sustainability.

The paper suggests that leading data centre operating companies may need to extend their sustainability reporting and to introduce external assurance procedures.

The paper provides an accessible review of the sustainability agendas being pursued by the UK's leading data centre operators and as such it will interest academics, students and practitioners interested in both sustainability and data centre operations and developments.

As smart cities flourish amidst rapid urbanization and information and communication technology development, the demand for building more and more data centers is rising. This paper aims to examine the principal issues and considerations of data center facilities from the cost and benefit dimensions, with an aim to illustrate the approaches for maximizing the net benefits and remain “green.”

A comprehensive literature review informs the costs and benefits of data center facilities, and through a case study of a developer in Hong Kong, the significance of real estate costs is demonstrated.

Major corporations, establishments and governments need data centers as a mission critical facility to enable countless electronic transactions to take place any minute of the day. Their functional importance ranges from health, transport, payment, etc., all the way to entertainment activities. Some enterprises own them, whilst others use data center services on a co-location basis, in which case data centers are regarded as an investment asset. Real estate costs affect their success to a great extent, as in the case of a metropolitan where land cost forms a substantial part of the overall development cost for data centers.

As the financial information of data center projects are highly sensitive due to the competitive status of the industry, a full set of numerical data is not available. Instead, the principles for a typical framework are established.

Data centers are very energy intensive, and their construction is usually fast tracked costing much to build, not to mention the high-value equipment contents housed therein. Their site locations need careful selection due to stability and security concerns. As an essential business continuity tool, the return on investment is a complex consideration, but certainly the potential loss caused by any disruption would be a huge amount. The life cycle cost and benefit considerations are revealed for this type of mission-critical facilities. Externalities are expounded, with emphasis on sustainable issues. The impact of land shortage for data center development is also demonstrated through the case of Hong Kong.

The purpose of this paper is to explore what information systems are used within the data center industry and to develop a data center information systems classification framework for facilitating the investigation of the dimensions, operational characteristics, and value attributes of the data center information systems.

A preliminary data center information systems classification framework is proposed based on a comprehensive review of existing information systems research. A case study of five data centers in Australia and a review of the practitioner literature in data centers are conducted. An open coding method is adopted for facilitating the analysis of the data using existing content analysis techniques.

In total, 19 different types of data center information systems are identified in data centers. These information systems are being used for managing the information technology and the facility infrastructure in the data center and for improving the agility, the operational and cost performance, as well as the environmental footprint of individual data centers.

The paper builds a data center information systems classification framework to examine the role, portfolio, managerial function, and utilization of data center information systems. Such a classification would provide a foundation for further investigating the adoption of various information systems in data centers towards the continuous pursuit of sustainability development from different perspectives.

The paper provides data center managers with recent trends and issues regarding the adoption of information systems in data centers to manage their operations. It offers data center management a valuable means for exploring innovative ways of effectively adopting various technologies to improve their performance.

Given that there is limited research on what and how information systems are used in data centers, the paper can be viewed as one of the few data center information systems investigations that provide a good comprehension of what information systems are used and what the value drivers are with respect to the use of information systems in data centers.