Data Center Industry Research Articles

Climate extraction and supply chains of data

The global data center industry relies on what this article defines as ‘climate extraction’. Through this peculiar but critical infrastructure for global Internet operations, a focus on Ireland reveals the entanglements of state, corporate, and environmental actors within the extractive calculations of transnational companies. Ireland has been advertised to and by data center developers because of its ‘cool’ climate while downplaying the importance of its low corporate tax rate and the government and planning system’s favorable treatment of big tech companies. Public discourses around big tech ‘greenwash’ power and contribute to a material climate (both atmospheric and infrastructural) from which value can be extracted. This is achieved by extracting for and from data circulation through the built and ‘natural’ environment. This article articulates the ways in which the spatial development of data centers as ‘strategic infrastructure’ contributes to the ongoing naturalization of capital and state power’s entanglements with the so-called natural world through technological systems.

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

The power consumption of cloud data centers has a considerable impact on the environment and climate change nowadays. Researchers are seeking to find practical solutions to reduce power consumption in data centers while guaranteeing the desired level of services and service level objectives. With the establishment of the data center industry, the demand for computation and data storage has been continually rising. Energy efficiency is one of the most significant issues faced by these big data centers to meet such high computational requirements. There are many industry acceptable metrics available such as PUE, DCiE, DCP, etc. Power Usage Effectiveness (PUE) metric has proven to be the most popular in measuring energy efficiency; however, it measures the power efficiency alone with no consideration for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions and the costs involved in total power usage across data centers. In this article, we proposed a novel Power Efficiency Measurement Calculator (PEMC) that combines and calculates the power efficiency, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, and the total annual costs incurred. The pseudocode and algorithm to perform these specific PUE, DCiE, and CO2 emission functions are given to explain the working of proposed work. Finally, the proposed PEMC calculator was tested and validated through a case study performed in one of the tier-level data centers in Malaysia and the results demonstrate its effectiveness compared with Power Usage Effectiveness (PUE) and other known calculators.

Integrated Stochastic Energy Management for Data Center Microgrid Considering Waste Heat Recovery

The rapidly developing data center industry results in a large amount of energy consumption. Considering its unique demand characteristics, it becomes desirable to improve its energy efficiency by reusing its waste heat. In this paper, an integrated energy management scheme is proposed to optimize the operation cost for a data center microgrid. The waste heat recovered from data center operation is optimally scheduled with other resources in the integrated energy management model to minimize the operation cost of data center microgrid. Taking the uncertain nature of renewable supply, power and heat demand into consideration, a two-stage stochastic programming model is formulated and analyzed under different test cases. The numerical study results demonstrate the effectiveness of the proposed integrated energy management model.

Data centres as logistical facilities: Singapore and the emergence of production topologies

Data centres mobilise server-client architectures to disperse and draw in labour from across industries and nations. In doing so, they provide an infrastructural fix for capitalist actors seeking to bypass traditional labour actions, by designing logistical routes around which to redirect production processes. In this article, we build on research that investigates the data centre industry in Singapore to consider how these facilities drive processes of global circulation and establish new kinds of labour relations and processes. We point to limits in conceptualising these relations according to dominant models of the supply chain or the production network. We argue that understanding the client footprint enabled by data centres as a form of territory allows us to approach these facilities as political institutions that influence the operations of power across wide geographical vistas.

How Do Data Centers Make Energy-Efficiency Investment Decisions? Qualitative Evidence from Focus Groups and Interviews 1.

The data center industry is one of the fastest growing energy users in the US. While the industry has improved its energy efficiency over the past decade, engineering analyses suggest that ample opportunities remain to reduce energy use that would save firms money. This study explores whether and why data centers might limit investment in energy efficiency. Given the scarcity of empirical data in this context, we conducted focus groups and interviews with data center managers to elicit information about factors affecting their investments and used content analysis to qualitatively evaluate the results. Split incentives between departments within companies and between colocation data centers and their tenants, imperfect information about the performance of new technologies, and tradeoffs with data center reliability were the most pervasive factors discussed by participants. While we find some evidence that market failure explanations such as split incentives and imperfect information had a limited role in slowing adoption for participants, rival explanations such as the cost of acquiring context-specific information, and opportunity costs associated with alternate uses of funds or highly valued attributes played a larger role in slowing investment in energy efficiency.

A Systems Overview of Commercial Data Centers

Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.

Analysis of a district heating system using waste heat in a distributed cooling data center

With the rapid development of the data center industry, a major source of waste energy is created through the increasing demand for cloud-based connectivity and performance. A recent study demonstrated that data centers are responsible for approximately 1.3% of total global electricity usage. In addition, nearly 40% of this power is consumed by computer room air conditioning (CRAC) systems for cooling the servers, which creates a significant stream of waste heat. The waste heat in a data center has a low quality; therefore, recovery and reuse of the waste eat is a challenge. In this study, a district heating system solution using waste heat in a data center was introduced. A distributed cooling solution was discussed to improve the quality of this waste heat. Then, heat pump technology was used to further raise the quality. The energy-saving effect was theoretically compared with coal-fired heating. A new index PUEwh was defined to evaluate the power utilization effectiveness of the data center with the heat recovery system. This waste heat district heating solution was implemented at a data center in Hohhot, China, and measurements demonstrated that the PUEwh of this data center was 0.13.

Data centre sustainability – Beyond energy efficiency

Data centres are large energy consumers, which have become more energy efficient in recent years due to increased awareness of opportunities, increasing energy costs and corporate social responsibility pressures. A lifecycle assessment approach identifies two other significant areas of impact: the embodied impact of IT equipment and mechanical and electrical plant, and the electricity source used in operational and embodied processes. Many data centre operators publicise their energy efficiency, some also report on their carbon footprint. However, there is a need for simple tools in order to help operators better understand and quantify the embodied impact and inform green procurement. Focussing purely on energy efficiency may cause a burden shift, e.g. by replacing equipment with more efficient equipment but increasing the embodied impact. The total environmental impact could remain the same/increase, but with the perception of a ‘greener’ data centre. Although currently there is limited data available for data centre life cycle assessments and the process is resource intensive, research has identified which factors significantly impact a facility’s environmental impact. This knowledge should be used in the design process and throughout the data centre lifecycle to minimise data centre environmental impact. Practical application: As the data centre industry continues to grow and its sustainability receives closer scrutiny, it is important to increase awareness of where its highest environment impacts lie and analyse the factors which influence this. This can help inform policy and decision-making to support the design and operation of data centres which are truly more sustainable. It is important that this research is not just theoretical but can translate into practical actions which can be implemented in a cost-effective manner for the benefit of everyone. The current focus in industry has been energy consumption and energy efficiency; many practices which were considered innovative a few years ago have now become standard best practice. It is time to examine which other areas should be prioritised for improvement.

Air Flow Measurement and Management for Improving Cooling and Energy Efficiency in Raised-Floor Data Centers: A Survey

Recently, the rapid growth in both data center power density and scale poses great challenges to the cooling system. On one hand, the data center operators try to over provision cooling resources for fear of server failures induced by accumulated heat. On the other hand, they also want to reduce the energy cost as the cooling system takes up a significant portion of overall energy consumption. Among all available cooling solutions, air cooling dominates the data center industry due to its simpleness. However, its cooling efficiency has been questioned due to the low air density and specific heat. In this paper, we provide an overview for current endeavors to improve the air cooling efficiency. We group existing researches according to the locations where they can be applied from the perspective of air flow cycle. We also discuss the thermal measurement issues. We hope this paper can help researchers and engineers to design and control their data center air cooling systems.

Design and economic analysis of liquid cooled data centres for waste heat recovery: A case study for an indoor swimming pool

A vast amount of waste heat is produced in urban areas from a range of local sources such as metros, large buildings and urban waste. Data centres are another rapidly growing sector generating heat that could potentially be recovered and reused for heating and cooling. The integration of heat reuse solutions in the data centre industry will decrease the operational expenses by reducing the cooling demand and will create new business models by selling the excess heat to nearby heat demand applications. On one hand, the manuscript demonstrates how liquid cooled data centres can reduce the overall data centre consumption up to 30% in comparison with state-of-the-art air cooled data centres. On the other hand, the liquid cooling configuration of on-chip servers is evaluated numerically for a case study of an indoor swimming pool. For the best favourable solution, the data centre operator reduces its operational expenses and generates additional incomes by selling the excess heat, achieving a net present value after 15 years of 330,000 €. Moreover, the indoor swimming pool operator reduces its operational expenses 18%. Finally, the results of the case study are extrapolated to study the impact of heat reuse usage in Barcelona.

Energy and Exergy Analysis of Modular Data Centers

The data center industry focuses on initiatives to reduce its enormous energy consumption and to minimize its adverse environmental impact. Modular data centers provide considerable operational flexibility in that they are mobile and are manufactured using standard containers. This paper develops steady-state energy and exergy destruction models for modular data centers with the open-source EnergyPlus software package. Three different cooling approaches are examined: direct expansion (DX) cooling, evaporative cooling (direct evaporative cooling, DEC, in this study), and free air cooling (air-side economization in this study). This paper shows that for hot and arid climates like those in the southwestern U.S., augmenting DX cooling with evaporative and free air cooling can result in energy savings of up to 38% and 36%, respectively. This paper also applies exergy analysis to suggest that the Energy Reuse Effectiveness of the data center increases with decreasing ambient (outdoor) temperature and increasing server inlet–outlet temperature difference. Furthermore, simulations indicate that the use of passive cooling techniques (e.g., DEC and free air cooling) decrease data center heating, ventilation, and air-conditioning energy consumption, except in extremely hot and humid climates.

Energy and exergy assessment in a perimeter cooled data center: The value of second law efficiency

The Data Center Industry remains in steady growth with worldwide energy consumptions in the order of hundreds of TWh, and yearly growth rates higher than the growth of global electricity consumption. Electronic cooling represents an important part of a Data Center’s energy demand, thus efficient thermal management practices have become critical in recent years. The Coefficient of Performance (COP) appears as the most commonly used metric when assessing the energy efficiency of a data center, although sometimes it proves insufficient to address irreversibilities in a system. In this work, the information provided by the COP is complemented with a second law efficiency analysis as a way to measure inefficiencies. The software Engineering Equation Solver (EES) is utilized to apply energy and exergy balances in each individual component and the global refrigeration system, simultaneously. The study considers variations in the number of active racks and the increase in hot air recirculation within the room, assuming that the approach closely represents the first years of operation. Maximum efficiencies are achieved when all racks are active and when aisle air containment prevents recirculation. Fewer active racks increase the exergy destruction, due to excessive power usage in a low demand situation. The chiller accounts for the highest power consumption with high density racks; whereas for low density racks, the cooling tower consumes most of the power. The second law analysis justifies the chiller power consumption based on its cooling requirements, and elucidates oversizing in the cooling tower power consumption. The COP increases with hot air recirculation, which could mislead the design towards inefficient decisions; the second law efficiency shows an inverse relationship with the recirculation, thus properly capturing the inefficiencies and better guiding the design. Appropriate energy efficiency analyses should consider both thermodynamic approaches (first and second law) to ensure proper use of working fluids cooling potential and maximize the system energy efficiency.

Energy model optimization for thermal energy storage system integration in data centres

In the last years the total energy consumption of data centre industry has experienced a rapid increase due to the vast growth in internet usage. This phenomenon has aroused the interest for both, data centre industry and researchers in energy consumption and carbon footprint mitigation. Data centres operational expenses are also a key parameter for the productivity and competitiveness of the industry. In the present work, the implementation and optimization of thermal energy storage (TES) into a real data centre is presented. To the knowledge of the authors this is the first time that a methodology based on TraNsient System Simulation (TRNSYS), GenOpt and the application of Hooke and Jeeves optimization algorithm is used to study the implementation of TES, in particular water storage tanks, in data centres. A sensitivity analysis on the TES system cost is performed in function of the available literature information. The results show that the operating temperature of the chillers should be as lower as the system allows and therefore this open the possibility to study ice storage system. Regarding the economic analysis, the data centre electricity expenses can be reduced annually up to 3%. The uncertainty of the electricity price in the future is a major issue and it is also discussed.

Evaluation of non-linear power estimation models in a computing cluster

The data center industry is responsible for 1.5–2% of the world energy consumption. Energy management technologies have been proposed for energy-efficient scheduling of computing workloads and for allocating resources in such computing infrastructures. One of the important factors for this energy management is the estimation of power consumption as a result of the workload schedule to be carried out. The commonly used power models are a linear function of resource features. Based on measurement data sets from our cluster, we extended power model study to multiple non-linear dependencies. We provide several novel contributions: we apply neural network models and unsupervised classification models with basic OS-reported resource features for power estimation; we build and test the power estimation models in a cluster environment from a large size of measurement data; we evaluate the power estimation models in terms of not only accuracy but also portability and usability.We prove that a multiple-variable linear regression approach is more precise than a CPU-only linear approach. The neural network approaches have a slight advantage – their mean root mean square error is at most 15% less than that of the multiple-variable linear model. The neural network models have worse portability when the models generated on a node are applied on other homogeneous nodes. Gaussian Mixture Model has the highest accuracy but requires the longest training time. In the end, we prove that models trained using the system-level full features have the highest accuracy comparing to only use part of features.

Experimental and numerical analysis for potential heat reuse in liquid cooled data centres

The rapid increase of data centre industry has stimulated the interest of both researchers and professionals in order to reduce energy consumption and carbon footprint of these unique infrastructures. The implementation of energy efficiency strategies and the use of renewables play an important role to reduce the overall data centre energy demand. Information Technology (IT) equipment produce vast amount of heat which must be removed and therefore waste heat recovery is a likely energy efficiency strategy to be studied in detail. To evaluate the potential of heat reuse a unique liquid cooled data centre test bench was designed and built. An extensive thermal characterization under different scenarios was performed. The effective liquid cooling capacity is affected by the inlet water temperature. The lower the inlet water temperature the higher the liquid cooling capacity; however, the outlet water temperature will be also low. Therefore, the requirements of the heat reuse application play an important role in the optimization of the cooling configuration. The experimental data was then used to validate a dynamic energy model developed in TRNSYS. This model is able to predict the behaviour of liquid cooling data centres and can be used to study the potential compatibility between large data centres with different heat reuse applications. The model also incorporates normalized power consumption profiles for heterogeneous workloads that have been derived from realistic IT loads.

Economics of the Interconnection Data Centre (IDC) Industry

The modern digital economy is characterized by high data volumes and the necessity for reliable high speed data exchange. Businesses such as Netflix, providing video on-demand, social and messaging networks that enable text, photo and video communications such as WhatsApp, Snapchat and YouTube, location based mobile applications such as Foursquare, cloud computing providers such as Amazon Web Services and Microsoft Azure, or search providers such as Google and Microsoft Bing all rely on high performance data transmission and data exchange between their own networks and the networks used to reach their customers or partners in order to provide services. The same is true for other industries e.g. high frequency trading that relies on an extremely fast exchange of trading data from stock exchanges to traders or brokers and online advertising which relies on high speed exchange of data from content and service providers such as search engines and publishers to advertising platforms. High speed and high quality data exchange between the networks of different companies can be technically achieved by direct physical connections via fibre optic or copper cables over short distances between the servers of two networks. Such physical connections are referred to as Direct Circuit Interconnections or Cross-Connects and take place in Interconnection Data Centers (IDCs). Enabling high-performance interconnection through Cross-Connects is the core value adding service offered by the IDC industry. Cross-Connect services are related to but distinct from the services of Internet Exchanges (“IXs”). IXs offer interconnection not via dedicated physical cables between two networks, but via a Network Switch that operates as an open exchange platform where many networks can simultaneously interconnect to many others. While interconnection via such a Network Switch has cost advantages when an organisation seeks interconnection with many networks, each of which generates only limited traffic, it cannot provide the same performance in data exchange in terms of quality of service (“QoS”) and security as Cross-Connects. The IDC industry provides essential infrastructure and services to organisations seeking to interconnect their networks and computing resources. The availability of high quality IDC services in sufficient quantities and on competitive terms is critical to the present and future increasingly interconnected digital economy. Interconnection is required to deliver digital content and applications and a large number of other services to users across Europe and the world. Given the importance of the IDC industry, this paper takes a closer look at the economic features of the industry that affect the dynamics and functioning of IDC markets. The paper discusses (horizontal) competition between IDC providers within the same IDC market and analyzes the (vertical) relationship between the IDC market and the IX market.

Impact of aisle containment on energy performance of a data center when using an integrated water-side economizer

The main purpose of this study is to analyze the impact of cold aisle containment on the thermal management performance of a data center, as well as on the amount of energy consumed for cooling. The thermal management performance of the aisle containment architecture, which has been widely employed throughout the data center industry, is demonstrated by experiment. A computational fluid dynamics (CFD) modeling strategy is used for the containment air leakage prediction validated by experiments. The thermal management performance of an uncontained and contained aisles are compared through CFD simulation. In addition, since the containment's energy performance is maximized when an economizer is incorporated, a model-based simulation of the annual cooling energy consumption of a modular data center with an integrated water-side economizer is conducted. The simulation results show that the thermal management performance is excellent in both architectures with an appropriate cooling system control, but the energy savings are much greater with the contained architecture.

Virtualised load management algorithm to reduce CO&lt;SUB align="right"&gt;2 emissions in the data centre industry

The environmental footprint of ICT continues to increase. Data centres are key contributors of greenhouse gas emissions that pollute the environment and cause global warming. All data centres are overwhelmed with numerous servers as the major components of processing. These servers and other equipment consume high amounts of power, thereby emitting CO2. In an average server environment, 30% of the servers are 'dead' and only consume energy, but such servers are not properly utilised, in which their utilisation ratios range from 5% to 10%. This paper proposes a new algorithm to manage and categorise the workload of different underutilised volume servers properly to increase their utilisation capacity. The proposed algorithm helps apply server consolidation methodology and increases the utilisation ratio of underutilised servers by up to 50%, thereby saving high amounts of power and reducing greenhouse gas emissions by up to 88%.

A First Look at Colocation Demand Response

Large data centers can participate in demand response programs\ and receive financial benefits by reducing energy consumption upon utility's request. However, the existing research has only considered demand response by owner-operated data centers (e.g., Google), leaving out another distinctly different yet integral part of the data center industry --- multi-tenant colocation data centers (a.k.a., colocation or "colo"), where the space is shared by multiple tenants for housing self-owned servers. A major hurdle hindering colocation demand response is "split incentive": the colocation operator may desire demand response, but lacks control over the tenants' servers; the tenants, on the other hand, can reduce server energy consumption but may not desire demand response unless they are properly incentivized. In this paper, we present a first-of-its-kind study on colocation demand response and propose an incentive mechanism, called iCODE (incentivizing COlocation tenants for DEmand response), which breaks the split-incentive barrier for colocation demand response. iCODE allows the tenants to voluntarily bid for energy reduction when demand response is needed and receive monetary rewards if their bids are accepted. We formally model tenants' bids and how the colocation operator decides the winning bids to maximize total energy reduction without profit loss. We demonstrate the potential of colocation demand response by using a trace-based simulation to show that iCODE can significantly reduce energy consumption (e.g., up to over 50%) during demand response periods.

Simplified server model to simulate data center cooling energy consumption

This paper proposes a server model that can be applied to simulating the annual cooling energy consumption of data centers and an analysis of its impact. Recently, aisle containment architecture and the variable air volume system have been widely adopted in the data center industry because of their superior indoor thermal management and cooling energy savings. However, the current simulation method, which fixes the server heat generation and the supply and return air temperature difference of the computer room air handler (CRAH), does not correctly reflect current cooling systems. In this study, a server model was developed that accounts for server thermal characteristics – including server power, fan airflow, and exhaust temperature – according to the given CPU utilization and temperature. After the proposed server model was validated, the annual cooling energy consumption was simulated for modular data centers with an air-side economizer and high ambient temperature. Unlike the conventional method, the proposed model showed that the cooling energy consumption increased when the CRAH supply air temperature was higher than 19°C because of the increase in fan energy consumption.