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This article is part 3 of the three-part series examining the main challenges of acquiring, implementing and using data center infrastructure management (DCIM). Part 1 presented a broad review on the different functions of DCIM in light of the operational challenges in the data center. Part 2 presented a possible method to expand DCIM from a data center management tool to manage IT, capacity, energy and cost. This final part addresses the applications of computational fluid dynamics (CFD) alongside DCIM.

Integration of DCIM With CFD

Part 1 of this series highlighted a number of instances where DCIM products tried to deliver add-on modules to their capacity-planning and analytics functions. Some of these modules can be dressed up as computational fluid dynamics (CFD), driven by primitive analytics, that will more often than not mislead the operator. CFD is a powerful and well-respected tool used by many industries to study airflow physics and heat transfer. In the data center industry, one can argue that CFD is often misused and misunderstood.

Part 1 also discussed the ability of DCIM platforms to integrate with other systems. CFD is trying to serve many functions in the data center, some of which are discussed in more detail here. Figure 1 shows a modified version of the DCIM workflow model (previously presented in Part 1), based on integration with a standalone CFD package.

DCIMFigure 1: The modified DCIM workflow model and integration with established CFD packages.

Prediction of airflow and temperature using turbulence models and numerical simulations is a complex problem and the subject of continuing research. The turbulence models that represent the unresolved terms in the time-averaged momentum and continuity equations (Figure 1) have been the subject of research for the last 30 years and have led to development of more-robust CFD solver and meshing technology. More often than not, the DCIM add-on modules that look like CFD are based on interpolation (trending) to predict the impact of a change—for example, install of a server into a cabinet and predict the temperature effect at a click of a button. The interpolation model in many of these DCIM add-ons assumes fixed conditions for the previous state to predict the future state, which is as good as a “fingers crossed” approach and often yields erroneous results.

CFD and the Tetris Effect

The application of CFD at the design stage to study different cooling scenarios, load densities, new technologies, airflow containment and open racks is well understood. The challenges are at the operations stage, however. IT equipment can be deployed on the basis of the three criteria—that is, space, power and cooling. The availability of space and power is easier to determine, thus enabling identification of a cabinet with available U-slots and provisioned power capacity from a power-distribution unit (PDU). The third element, cooling, is difficult to determine.

TetrisFigure 2: The Tetris effect and its similarities to operational planning in data centers: (a) playing Tetris where the blocks and board are known upfront, and (b) playing Tetris with a sequence of random blocks.

IT managers will deploy and fill out servers in the space while remaining blind as to where the cooling capacity is. As a result, changes in IT configurations in the space are generally invisible to the engineering and facilities teams. The result is the Tetris effect (see Figure 2), where any plan for coordinating the blocks is invalidated as soon the game starts. Now imagine blindly carrying out a plan based on square blocks while ignoring the fact that the blocks are changing. Data center operators commonly follow a plan despite changes to the IT units (blocks). The end result is that the operator arrives at 100% of the data-hall cooling capacity while at 70% of design power capacity, shown in Figure 2b. As mentioned in the preceding parts of this series, the data center owner is concerned with using the maximum capacity in their facility. If they designed and built a 1,000 kW facility, they intend to use all 1,000 kW.

workflowFigure 3: The data center politics chain and the motivations of the different disciplines.

To effectively apply the space, power and cooling determinations, the intelligence and exchange of information between the facilities and IT disciplines must be addressed (see Figure 3). If this communication breaks down, the consequences are catastrophic, resulting in the Tetris effect in the deployment of IT capacity shown in Figure 2b.

The Complexities of Airflow in the Data Hall

The misconception is the air from floor grills will cool the servers. They may do so in the correct application of airflow-containment strategies, but the IT server draws air from the path of least resistance. For a typical floor grill (600mm square) the airflow can vary from 300 to 600 l/s. In general, the higher the static pressure under the grill, the greater the airflow, and the lower the static pressure, the less the airflow (see Figure 4). In cooling terms, this situation can correlate to 3–6 kW of cooling per grill, and generally anything lower than 300 l/s may provide insufficient pressure at raised-floor level to cool the servers in a cabinet; anything higher than 600l/s may simply bypass the cabinets altogether (overshoot).

airflowFigure 4: The static pressure principle behind grill air flow.

Figure 5 demonstrates the principle. The cooling units on the left side of the data hall in Figure 5(a) induce a velocity jet in the floor void, which causes the low-static-pressure regions identified in Figure 5(b). These regions correlate well with the reduced net flow from the affected floor grills identified in Figure 5(c).

CFDFigure 5: Factors influencing airflow from floor grills: (a) floor-void velocity, (b) floor-void static pressure and (c) grill flow.

The concern here is in addressing the high-velocity jets from the scoops on the cooling units. Other common considerations include the management of floor tiles in relation to the IT load—for example, determining the appropriate number of floor grills to maintain reasonable static pressure from cold aisles in front of cabinets. Too many grills can reduce static pressure in the floor void and starve cabinets of air from the cold aisle, promoting the undesired effect of second-hand air cooling.

The second complexity that must be addressed is temperature. The temperature distribution in the data center depends on several factors, such as cooling-unit configuration, airflow, spatial configuration of servers and load density. Data center owners commonly install cold-aisle containment in their data halls. This approach is quite often sold to them with the energy-savings tag or to increase reliability at higher operating temperatures by reducing mixture of cold and warm air. More often than not such installations blindly eat into the availability of cooling capacity elsewhere in the data hall and may present only a superficial return on investment (ROI) to the data center owner. Figure 6 shows such an example, where the cold-aisle containment pods are overcooled to 18°C, whereas equipment in the legacy region experiences temperatures close to 24–26°C. In this case, the cooling units close to the legacy equipment are working less hard than those serving the cold-aisle containment pods, resulting in the temperature differential observed in the floor void

CFDFigure 6: Understanding complex interaction between air-containment cabinets and open architecture equipment.

Challenges of Using CFD

Even the most established CFD packages face challenges. The first and most obvious is computational power, which was a more prominent issue 10 years ago. But with the development of turbulence models and smart grid meshing, time-averaged CFD can be conducted relatively speedily on a good modern laptop with a high-spec processor, plenty of ram and a decent graphics card to view the output. To be clear, we are referring to hours, not the seconds that some vendors claim. If the output takes minutes to seconds, it is likely using short cuts, primitive meshing and analytics, which can lead to unreliable results.

airflowFigure 7: The CFD workflow process.

The use of unsteady CFD to model temporal behavior in the data hall—that is, analysis of the rate of increase of temperature following a site power interruption—can also be carried out using the same hardware, but at a greater computational time.

The next important challenge is calibration. In general the output from a CFD model can be very detailed, but it’s based on many assumptions. This situation may be acceptable during the design phase for an existing site, but following the construction of a model, the model needs calibration against the facility parameters to establish a starting baseline. This effort may include matching the airflow volume from the grills, followed by the temperature and flow parameters at the cooling units, against the model. Skipping the calibration process can result in the conveyance of unreliable information to the data center operator and can have harmful consequences on their operations.

The most common problem is a third party carrying out a six-month CFD analysis and advising on capacity planning, hot spots and so on. This approach results in repeated, unnecessary setup costs, which are usually passed on to the data center operator. Given the rapid changes in a modern data center, the expiry date on a calibrated model is no more than two to three weeks. Therefore, to get the full value of CFD, the analysis must remain in house and within the workflow of the facilities and IT teams (see Figure 7) and must continuously aid the decision-making process. The ongoing recalibration process is part of the engineering cause and effect that ties the IT and facilities teams into the data center politics chain (see Figure 2).

Using CFD as an Operational Tool

An important part of the engineering cause and effect are the changes outside the whitespace (i.e., equipment maintenance) that can result in consequences to the IT equipment, and vice versa. For example, the maintenance of a power-distribution board feeding the cooling units in the data hall, and the effect on the environment with N cooling units in operation during the maintenance mode. Use of CFD to assess the impact of such scenarios on the data hall, before implementation, is well documented. One of the most important operational processes is regular assessment of space, power and cooling, as well as determining where to install new servers.

CFDFigure 8: Use of CFD to test the IT deployment strategy: (a) facility model and IT inventory, (b) updated server power plot and (c) predicted cabinet air-inlet temperatures.

In the example shown in Figure 8(a), the data center operator has a virtual model of the facility with the IT inventory. The IT manager identifies the requirement for new servers to be deployed, and installs them in cabinets on the basis of available space and power (Figure 8b). The virtual facility model, however, predicts that such a configuration will create cooling problems as Figure 8(c) highlights, where the circled region indicates average server air-intake temperatures greater than 27°C. The facilities personnel are then responsible for identifying the appropriate location for these servers using the virtual model and for coordinating with the IT manager. This effort may involve several iterations of the workflow model in Figure 7. The end result is an ever evolving cooling map of the facility as Figure 9 shows, with cooling availability across different cabinet locations.

Needless to say, in some situations—such as the uniform application of airflow containment or data halls with ducted hot-aisle return to cooling units (no mixing of the airstreams)—common sense can go a long way. For those scenarios with staggered equipment in open air and mixed airflow containment with legacy equipment, CFD as an operational tool is not a nice to have but a necessity to fully utilize the data center design IT load.

data centerFigure 9: Releasing stranded capacity. Identifying available rack capacity based on data-hall cooling distribution and existing installed servers.


This article has highlighted some of the data center challenges addressed through the correct use of CFD. This technology addresses the bidirectional transfer of intelligence from the IT teams to the operations teams.

The discussion reviewed the importance of integrating with an established package, rather than an add-on module to the existing DCIM product. The solver and meshing technology lie at the core of the CFD engine. Although these elements are both hidden from the user, they are developed to be robust and adaptable to different data center environments. Even the most established CFD packages face challenges in solving the complex airflow physics problems in the data hall.

CFD is best kept in house with the data center operator as an operational planning tool, as opposed to outsourcing simulations. This approach allows the operator to regularly predict the physical response of the data center in different scenarios in a safe offline environment before deployment. But more importantly, it helps avoid the classical Tetris effect in IT-deployment planning. Most importantly, the continuous calibration of the model is pertinent to unlocking the full benefits of CFD for the IT and facilities teams.

Leading article image (adapted) courtesy of Charlesreid1  under a Creative Commons license

About the Author

infrastructure managementEhsaan Farsimadan is Director of Engineering at i3 Solutions Group. He previously worked as a technical consultant for the Uptime Institute, being responsible for data center design and facility Tier certifications. Before Uptime Institute, he served at Romonet as Head of Modelling and Customer Engineering, being responsible for the company’s client engineering and consulting engagements worldwide. He is a mechanical engineer with diverse industry experience. Ehsaan also previously worked as an M&E design consultant at Cundall, where he was responsible for developing data center concepts to scheme design in addition to leading the modeling team. He has developed specialities in data center predictive modeling and IT services, and he has good knowledge of electrical systems. Ehsaan is a chartered engineer accredited by the IMechE. In 2008, he obtained his doctorate (PhD) in mechanical engineering and also holds a bachelor’s degree in mechanical engineering with aeronautics. He has made significant contributions to the field of turbulence research and the data center industry through publication of a number of journal papers.

The post DCIM Part 3: Using CFD Alongside DCIM appeared first on The Data Center Journal.


This article is the second of a three-part series examining the main challenges of acquiring, implementing and utilizing data center infrastructure management (DCIM). Part 1 presented a broad review on the different DCIM functions in light of the owner’s operational requirement in the data center, highlighting the challenges in monitoring, capacity-management, analytics and reporting. This part presents a method for expanding DCIM from a data center management tool to manage IT, capacity, energy and cost.

Tailored DCIM

Vendors who sell DCIM as a “cure-all” or “on-size-fits-all” solution rather than tailoring the system to the customer’s individual business requirements compound the problems faced by owners who want to move to DCIM. These “one-solution systems” aim to address all the focuses of IT and facilities, however, so they have often focused on one or two priorities but fail to meet all the functional needs of data center operators. To address this issue some DCIM vendors also provide modular packages that can be tailored to the customer. For example, a colocation provider may be more interested in control of HVAC-plant and power-management subsystems, whereas a small enterprise data center may want to directly monitor IT systems in the white space and rely on facilities staff to look after control and power.

The questions that data center owners must ask themselves are “What do I need to manage my data center?” and “What visibility do I need from my data center to manage ongoing operation and plan for the future?” Consider two different cases: data center (1) is a new build with rack PDU metering, and data center (2) is a legacy facility that has no rack power-monitoring capability, but it has been using an existing BMS to monitor infrastructure and spreadsheets in order to track sold capacity and customer power draw using manual readings. One can argue that the requirements of (2) are very different from those of (1). It may be that data center (2) will prefer to integrate the BMS with a DCIM solution that will also take inputs from manually assembled power-utilization spreadsheets, whereas data center (1) may want DCIM to collect live rack power data and monitor customer power usage per circuit. The operator of data center (2) may have identified its limitations and have a good understanding of its capacity limits, whereas data center (1) is relying on the DCIM platform.

The commonality between the data center providers is that they both want a sensible handle on their operating cost, efficiency, capacity management and forecasting capability.

modelFigure 1: The challenges of manually decoding the data center.

At some point in the data center life, and at some level in the organization, someone will try to forecast energy consumption and cost. This effort could be in the form of a spreadsheet built around a model of the facility (see Figure 1). It’s a labor-intensive task and will evolve into a continuous line of error-ridden spreadsheets that will ultimately be understood by only one person in the organization. To add to this difficulty, the problem is not static: equipment performance characteristics will vary will load, ambient temperature and operating conditions. The task is too onerous for a human to solve manually.

Adding Rocket Science to DCIM

To understand cost properly, one must solve the engineering problem first. This is where predictive modelling adds some rocket science to the analytics of the DCIM workflow model previously presented in Part 1 of this series (see Figure 2).

workflowFigure 2: The adjusted DCIM workflow model.

One thing to clear up is the misnomer that every reference to modeling is computational fluid dynamics (CFD). Part 3 will examine the operational benefits of CFD alongside DCIM. When we reference modeling in the context of plant performance, we are referring to the function of the plant; for example, the behavior of computer-room air-handling (CRAH) units with changing IT load, or chillers with changing ambient conditions and load.

The question is often asked, “How do we calculate or model the expected performance?” The answer is simple: the performance of every device in the data center can be determined by a mathematical model. The vendors have libraries of this information, whether it’s the load versus efficiency for a UPS, transformer, fan or a pump. Figure 3 shows the coefficient of performance (CoP) of a particular chiller against ambient temperature and load.

chillerFigure 3: Coefficient of performance for a chiller with varying cooling load and condenser-air entering temperature.

The main characteristics of the curve in Figure 3 are the sharp drop in CoP as condenser temperature increases beyond 30°C, as well as the small improvement in CoP as the cooling load approaches 100%. The complexities of the chiller behavior in Figure 3 represent a single subsystem component among the collective group of critical plant components that function in the data center. These components interact with each other in the distribution of power and heat in the facility. For example, the chiller sees a cooling load and draws power from the switchboard or panel serving it, representing the interaction between the mechanical plant and electrical plant.

The Skeleton Behind the Model DCIM

There is no single definition of what DCIM is or what it should have. The canard is that DCIM will fix your data center. The reality, however, is that it should provide a deeper view into the consumption and performance of the infrastructure and highlight areas of concern in the facility that need to be addressed before they become major risk items.

To unlock the ability to truly forecast cost and energy in the data center, DCIM should be able to provide a glimpse into the future and give the operator an idea of how the data center will react to different load and temperature conditions, as well as keep track of the utilized capacity of the major plant items. One way this feat can be achieved is through a performance model that encompasses the main mechanical and electrical infrastructure in the power and cooling chain (see Figure 4). This model replaces the previously onerous manually constructed model in Figure 1.

DCIMFigure 4: Data center performance model identifying the nuts and bolts of the power and cooling chain (blue connections represent the power chain, and red connections represent the heat chain).

The purpose of the model is to warn the facilities personnel whether the reading at the meter is a healthy one or whether it falls outside the acceptable range for that condition. Why is this capability important? How do we know the reading from the utility meter or the chiller submeter is correct? The common response provided by site personnel is “We calibrate our meters annually,” which misses the point entirely. The meter presents the reading as it is, but it doesn’t provide that layer of intelligence to inform the data center owner how far the reading is from the healthy value for that load and temperature condition. Think of the car dashboard as an example; the driver has all the actionable intelligence needed to navigate and make decisions before driving. Without this intelligence—e.g., the fuel meter and speedometer—it would be like driving in the dark.

Of course this model must tie in with the DCIM platform. The first things to identify are the main data center components or functions that require management. Then identify the primary metering points of the facility and, secondly, to what granularity the information needs to be recorded. It could be kW, amps, volts, Hz, RPM, °C, °F or relative humidity, but also important is an appropriate recording interval, such as 15 minutes, 30 minutes or hourly. Metering everything that moves and filling up disks with data that cannot relate back to any actionable intelligence is a poor investment. As a guide, choose the 1–10% of those meters that a human can realistically read and review. For example ask what you will actually do with per-rack metering when you only need to manage power at the row or PDU level.

meterFigure 5: Tracking the health of your data center against design performance: (a) nominating the appropriate meters and (b) tracking energy and cost over time.

In the example shown in Figure 5(a), the main meters have been nominated and the data center key performance indicators (KPIs) are tracked against the expected design performance (see Figure 5b). Where the differences start to grow in Figure (5b), a blind divergence occurs; it’s only visible by plotting the healthy expected design reading from the model. The common cause of divergence is manual intervention, where someone has changed a setting on the cooling system or the system has responded adversely to a load condition, but only to a degree that is not yet a risk item. If it is detected early as per Figure 5(b), the facilities personnel can attend to it promptly and attempt to recover the divergence gap and bring the data center back in line with the design.

The DCIM analytics and data storage functions should calculate and store the hourly predicted performance of the data center by analytically relating the energy interactions between the mechanical and electrical plant as per the power and heat-chain model in Figure 4. The inputs to this model are the measured data center IT load and external ambient temperature. This data can then be presented alongside the actual logged reading from the utility and or other plant meters in the facility (see Figure 5b).

DCIM as a Decision-Making Tool

The previous section discussed how DCIM could be equipped to assist facilities personnel in identifying risk and divergence by tracking the performance of the facility against the design, but also by prompting senior management or engineering to ask, “Did I get what I paid for?”

Given that the engineering problem can be solved by the performance model in Figure 4, reporting activity-based metrics becomes a trivial exercise. These metrics could include customer-level PUE, total cost, and delivery cost ($/kWh)—the types that provide business intelligence and decision-making capabilities to senior management. Figure 6 shows allocated metrics for each IT customer in the data center that was modeled in Figure 4. The output comes from the engineering model that considers energy overheads and is for the cost-based metrics of capital and maintenance costs. Consider Figure 6 (a); the allocated PUE of Customer IT (1) is much higher than that of IT (2) and IT (3). This result is an outcome of the activity-based performance model in Figure 4, whereby IT (1) is powered by a different string of UPSs and different type of CRAH units than IT (2) and IT (3).

PUEFigure 6: Enabling useful KPIs and business intelligence from your DCIM platform. The figure shows important data center metrics allocated across the three different IT customers in Data Hall 1 and 2: (a) allocation of PUE, (b) allocation of cost, (c) allocation of $/kWh and (d) the data center performance model.

The performance model therefore only allocates to IT (1) the contributing energy and cost overheads. Furthermore, the total cost in Figure 6(b) is lower for IT (1) compared with IT (2) and IT (3). This result could be due to IT (2) and IT (3) using higher IT load (i.e., more IT kWh but less energy overhead). Additionally, IT (2) and IT (3) can have more-onerous capital and maintenance costs tied to them, possibly from an upgrade project, than IT (1).

Figure 6(c) plots the allocated $/kWh for each IT customer according to their activity-based performance model. “Data Center Delivery Cost” is the honest way of reporting cost efficiency and can be more relevant than PUE to senior management. For example, in the first two to three years of life for a new facility, the capital-cost component is dominant regardless of the PUE and operating efficiency.

energyFigure 7: The data center delivery-cost metric.

As per the definition in Figure 7, the delivery-cost metric captures the amortized capital, maintenance cost and energy-utility cost against the kWh of IT load delivered. Using this definition of $/kWh, it’s easier to interpret the cost efficiency of customer IT (2) and IT (3) from IT (1), as Figure 6(c) shows. Customer IT (1) costs the business $1.6/kWh, whereas as IT (2) and IT (3) come in at $0.6/kWh and $0.8/kWh. Although the total cost of IT (2) and IT (3) is higher because they are using more IT draw (see Figure 6b), the cost efficiency of serving these customers is significantly lower owing to one or more of the numerator components in Figure 7. This information is, of course, very sensitive and should only be in the hands of senior management or sales teams. But it helps the business decide on potential renegotiation penalties and/or decide to release unused capacity from existing customers in order to sell space to new customers.

ITFigure 8: Using DCIM to assess data center upgrade options.

The model takes performance and data inputs from all the main plant items to allow for the assessment of data center capacity utilization at a component and/or system level. It helps the data center owner plan for upcoming infrastructure maintenance and enhancement programs, and it helps evaluate facility upgrade options without the need for long, complicated, error-ridden spreadsheets. Figure 8 presents a 10-year net-present-worth (NPV) calculation for a cooling-system upgrade, giving consideration to the influences on the energy overhead, maintenance cost and required capital investment. In this case, it’s easy for in-house engineering teams to assess upgrade options in a shorter time frame and rule out those that fail to present a cost-efficient business case. On the basis of Figure 8, installing a Turbocor Chiller will make little difference to the cost position over 10 years, whereas an indirect cooling system presents a saving of $600k.


This part of the series presents an approach that can complement DCIM analytics to accurately track data center energy, cost and capacity against design performance. The core intelligence of this approach is a smart model that can inform the data center owner of the impact of load, temperature and change on the facility.

This approach allows the data center owner to realistically manage the level of expectation from the facility with fair or otherwise achievable PUE and/or cost targets. But it also enables the owner to measure the influence of change in one subsystem component on the overall facility efficiency and cost, before any implementation. The output from the analytics will cater to the requirements of facilities personnel as well as senior management.

For this effort to be effective, the appropriate meters need to be nominated and output needs to be stored at appropriate intervals. Doing so requires some integration with existing BMS or DCIM platforms. The challenge eases owing to the ease with which data can be gathered from all the different hardware devices in the facility.

Leading article image courtesy of Tony Webster under a Creative Commons license

About the Author

infrastructure managementEhsaan Farsimadan is Director of Engineering at i3 Solutions Group. He previously worked as a technical consultant for the Uptime Institute, being responsible for data center design and facility Tier certifications. Before Uptime Institute, he served at Romonet as Head of Modelling and Customer Engineering, being responsible for the company’s client engineering and consulting engagements worldwide. He is a mechanical engineer with diverse industry experience. Ehsaan also previously worked as an M&E design consultant at Cundall, where he was responsible for developing data center concepts to scheme design in addition to leading the modeling team. He has developed specialities in data center predictive modeling and IT services, and he has good knowledge of electrical systems. Ehsaan is a chartered engineer accredited by the IMechE. In 2008, he obtained his doctorate (PhD) in mechanical engineering and also holds a bachelor’s degree in mechanical engineering with aeronautics. He has made significant contributions to the field of turbulence research and the data center industry through publication of a number of journal papers.

The post DCIM Part 2: Managing Data Center Costs appeared first on The Data Center Journal.


This article is the first in a three-part series examining the main challenges of acquiring, implementing and using data center infrastructure management (DCIM). Part 1 examines the different DCIM functions in light of the operational challenges in the data center. Part 2 presents a possible method to expand DCIM from a tool for managing the data center to one for managing IT, capacity, energy and cost. Part 3 addresses the applications of computational fluid dynamics (CFD) alongside DCIM.

What Is DCIM?

If you ask three people in the data center industry what data center infrastructure management (DCIM) means, you will most likely get three different answers. Although DCIM has many formal and informal definitions, there is no formal classification of DCIM tools, and therefore data center owner-operators are finding it difficult to distinguish between the “must have” functions for better risk and efficiency management, and the functions that are optional and add less value. There are numerous tools on the market that provide different focuses for different disciplines in the data center chain. Collectively these groups of tools are known as DCIM.

data center

Figure 1: The main areas of the data center served by DCIM.

Figure 1 shows some of the main DCIM areas in the data center, from the mechanical plant to the electrical plant, data-hall whitespace and IT equipment, but also security and integration in the building management system (BMS). DCIM systems can provide monitoring, control and other functions in some or all of these areas.

The Evolution of DCIM

Before the inception of the PUE metric by The Green Grid in 2007, data center owner-operators had little interest in tracking energy consumption. This situation changed with shifting organizational attitudes toward cost efficiency and social responsibility, and it has partly been driven by more focus from watchdog organizations focusing on data center energy consumption. The motivations of data center managers have gone further than providing security and 24×7 availability to satisfy their customers. More often than not, it’s the data center owner’s customers who are asking for greater visibility of their footprint, efficiency and delivery cost.


Figure 2: PUE monitoring.

Some of the early BMS systems with dashboards for monitoring PUE were the evolutionary forerunners of DCIM (see Figure 2). Over time DCIM has evolved into a broad category of systems used not just for energy monitoring but also for asset control, capacity management and other specialized functions. This evolution has led the products to be marketed toward a broader audience, often with confusing descriptions of both the system functions and their potential benefits. One corner of the market is overrun with products that are adaptations of traditional BMS platforms that aim to manage, monitor and optimize energy consumption and PUE. And looking at the DCIM marketplace as a whole, it is quite common that one vendor’s DCIM is different from another’s. Two vendors can claim to offer DCIM while providing two completely different products, focusing on different functions in the data center.

Distinguishing Between the BMS and DCIM

Traditionally the aim of DCIM is to help data center owner-operators manage and control their facilities. One can consider the BMS as the most basic representation of DCIM (see Figure 3), providing a view into plant monitoring, alarms and control. As data centers have become more complex and data center owners and operators have demanded more capabilities.


Figure 3: The BMS workflow model.

A number different models have been proposed in the industry to describe the DCIM workflow (Figure 4). The majority of them consider the mass collection and storage of data from sensors and meters. This data is used as an input to subsystems analysis, such as environmental control, power and energy management, security, and IT systems. The output displays on a dashboard for the data center operator. Whether this output provides actionable intelligence or just a colorful representation of the metered data is an area that must be addressed, both by the functions incorporated into the DCIM system and the way the operators and management use the system. How these questions are addressed by data center operators is the added value (if any) that DCIM provides over the BMS platform.


Figure 4: The DCIM workflow model.

Currently most DCIM products find it difficult to answer all the major questions that drove the data center owner to make the investment (see Figure 5).


Figure 5: Main questions asked by data center owners.

No one definition describes DCIM or what it should do. One incorrect assumption is that DCIM will fix your data center, and so the first question in Figure 5 is a symptom of product misrepresentation in the market. The reality is that one basic function of DCIM should be to provide a deeper view into the performance of the infrastructure and highlight areas of concern that need to be addressed before they become major risks. Most notably, these areas include whether the data center is operating per the design intent, the capacity limit (“choke points”) of the infrastructure, the assessment of capital projects, identification of stranded capacity and the allocation of a suitable location for the next deployment of servers.

Where Does DCIM Fit in the Organizational Structure?

The data center universe consists of two distinct categories of professionals: IT personnel that are responsible for the IT equipment in the data center white space (i.e., servers and network switches) and the infrastructure teams that are responsible for the facility and the white space. The second group operates the facility, whereas the first group owns the IT equipment. During the life of the data center, these two groups will become more and more intertwined in management and operation (Figure 6). And within each are different “doers and managers” who require different information from the same DCIM system.


Figure 6: The data center politics chain and the motivations of the different disciplines

As mentioned already, the challenge at the facilities level has been to identify the difference between existing BMS platforms and the benefits that DCIM offers. Traditionally the BMS sits on the left side of the chain in Figure 6 and gives facilities personal insight into the health of the data center by monitoring and in some cases controlling the plant.

In the chain of responsibilities, the functional and information requirements of C-level management may be different from those of technical engineering staff and/or site operations staff. The former may be interested in the reporting of high-level performance indicators for the business and trying to reduce operational expenditures, whereas site-level or technical staff could focus on reducing local site risk and assessing the feasibility of capital projects.

It’s the automation of the workflow process that DCIM is trying to address. For example, when an IT manager identifies the need to deploy IT servers, operations and facilities must allocate and provision the appropriate space, power and cooling. The core relationship DCIM is trying to address here is the critical interaction between the IT and facilities teams. The requirement is for a bidirectional flow of intelligence between the facility personnel and the IT personnel, and up and down the management scale from operators to senior management. This coordination and information “flow” is one of the main ways that a well-designed and well-implemented DCIM system creates a major benefit to the organization and provides a measureable return on investment (ROI).

The unfortunate marketing trend of recent times is DCIM vendors presenting ROI cases to their prospective clients based on pilot projects such as air-flow containment and/or higher operating temperature. Needless to say, these scenarios do not necessarily require DCIM and can be implemented on the merits of the existing BMS or facilities teams.

Dissecting the Different Components of DCIM

One of the most noticeable differences between BMS and DCIM is the visualization of the output; it’s the first element that is visible at an exhibition of the product. The users are given a dashboard (Figure 7) that can be customized to their requirements and display meaningful information, possibly relating to the data center subsystems, physical assets, capacity, and site alerts. Some vendors provide more insight into the IT equipment on their dashboards, while others focus more on the mechanical and electrical infrastructure.


Figure 7: The complexities of the modern DCIM dashboard.

or example one facility operator may prefer to see data-hall average temperature, IT load and PUE, whereas another may prefer to see a weekly breakdown of energy cost, with business performance indicators and the last five major alerts in the facility. Figure 8 shows the six must-have DCIM functions; they include monitoring, asset management and capacity planning (both at the facility and whitespace), analytics and reporting, integration with other systems, and the ability to collate data from other DCIM platforms for portfolio-wide management of the business.

data center

Figure 8: The six DCIM functions.

Portfolio-wide management addresses the local view of the data center in line with the global portfolio, offering the ability to view data center assets, status, energy and cost performance metrics at different business layers. The best dashboards provide overview or high-level information with the ability to easily “drill down” to more-specific layers.


Although a BMS may be equipped to monitor the major plant components, functions such as trending intervals, meter configuration, data storage and data analysis may be beyond its abilities. To manage the main subsystem components, a DCIM system must have an inherent capability to relate back to past performance while continuing to monitor ongoing operation. This capability requires reliable meters and sensors with appropriate disk space to gather, store and trend information. The primary subsystems that should be monitored are facilities power, environmental parameters, security and the IT space.

Facilities power includes monitoring of detailed electrical data such as power, voltage and amperage for the different subsystems, thus allowing engineering teams to manage and understand the electrical distribution but also supporting capacity planning and facilities expansion. Environmental control includes monitoring and control of all heating, ventilation and air-conditioning (HVAC) subsystems, including fire, gas and water systems. Security monitoring includes tracking of human activity in the data center, including identification. The IT space subsystem should monitor the power and cooling in the white space such that the service-level requirements of the data center operator and its clients are uncompromised. Many DCIM systems offer modules that allow reading and tracking of performance for individual IT components, providing data on utilization, energy consumption and so forth.

A highlighted weakness from the majority of DCIM solutions is the philosophy to meter and trend everything and anything that moves, but the finite amount of storage space usually results in data that is simply discarded after a month and is unavailable for trending over longer periods. Care must be taken in developing the design requirements for the system to consider the difference between data center information management and data center data management, to focus on useful information versus collecting data for data’s sake.

Asset Management

Asset management is an often overlooked but critical component of DCIM. The data center may contain thousands of assets, from IT equipment to power and cooling infrastructure. For the white space, this process may involve an inventory of all IT equipment, models of the data center rack layout and cabinet U-slot configurations. For example, the ability to quickly locate a server in the white space can reduce maintenance periods on equipment and increase overall availability. It’s not only about locating equipment, but detailed information about the equipment’s configuration, power source, control parameters and maintenance interval.

Capacity Planning

Capacity management through DCIM at a facilities level allows for insight into the utilization level of the electrical and mechanical plant. Being able to use important resources in the data center—in particular power and cooling—is critical. A data center owner that designed and built a 1,000 kW facility and intends to use all 1,000 kW. Further analytics of the metered data in some of these systems allows for change-impact analysis on the power and cooling infrastructure. In the white space, further focus must be placed on the identification of the optimal location where IT equipment can be installed through assessment of power, space and cooling. For colocation data center providers, “capacity” is the product that generates revenue, so managing this “inventory” is an obvious critical function. For enterprise data center owners, managing capacity is equally important both for operational cost control and planning of future capital-intensive expansion.

Analytics and Reporting

Many DCIM products are equipped with analytics (often hidden from the user) that employ past data with some trending and/or interpolation to determine the impact of change in the facility. Although the claim is that these tools help identify stranded capacity, the output can be rather hard to interpret and hard to translate into actionable plans for change management. Smart capacity planning can often postpone the requirement for the build-out of the next phase or next data center, thereby saving millions of dollars. The misconception in many DCIM solutions is that by storing masses of data for power consumption and IT load growth patterns, it’s possible to predict when a particular subsystem component is close to its limit utilization, signaling the requirement for an upgrade or capital project. In fact, this approach neglects the complex energy relationships between the major subsystem components and the sensitivity of the subsystem components to future IT load growth and varying ambient temperature.

Thus, the challenge is to have the ability to process the vast amount of data in an efficient and timely manner and provide recommendations in the form of actionable intelligence to the different disciplines. This information must be filtered with the target audience in mind; for example, the site manager may be more interested in risk items and energy consumption, whereas as the C-suite may want a high-level view of the operating cost and upcoming capital and maintenance projects for the site. Tracking the “right” data and having analytics that allow conversion of that data into useful information is critical. The DCIM vendor should aid in selecting the DCIM system modules that will meet the data center owner’s needs, and not try to sell unlimited capabilities and modules that will never be of use or that are already present in a separate system. For example, the use of CFD to manage capacity in the white space and provide insight into the impact of change to the data hall can be extremely valuable. The tendency of DCIM vendors, however, is to package their solutions with add-on modules that provide some form of modeling, as opposed to integrating with standalone CFD packages.

Integration With Other Systems

A sensible DCIM system is one that is aware of its limits. Typically, DCIM products implemented post-construction may need be integrated with existing BMS systems, either at the sensor/meter level or at the processor level. For example, a data center operator is likely to be comfortable with the BMS user interface and may find it confusing to look at two different software platforms. Management will have reports in either hard copy or electronic format that have for years tracked and evaluated data center performance. A new DCIM system should be capable of integration with existing systems on both the hardware and software level, including connection to existing meters and sensors, and be capable of importing existing data into the DCIM database. The transition from an existing BMS system to a new DCIM system may take years, and the DCIM vendor team will be critical to the success of that transition.


This review has highlighted some of the challenges facing data center owners and operators in selecting a DCIM in a large and still expanding marketplace. The aim of DCIM has been to close the communication gap between the different disciplines and address operational and management challenges in data center and IT organizations.

A broad review of DCIM functions highlights potential weaknesses in the monitoring, capacity-management, analytics and reporting functions, which are critical evolutionary advancements of DCIM over traditional BMS systems. Selecting the right mix of these advanced capabilities is a key to maximizing the ROI, which is the ultimate objective. The limitations and capabilities of the core analytics capabilities of DCIM products must be transparent to the customer. In extreme cases the analytics functions can be crippled by the requirement to process an overwhelming amount of data. Poorly chosen or poorly designed systems will suffer from the inability to present output in the form of actionable intelligence that aligns with the organization’s business objectives.

The key is to avoid add-ons that provide no value to the business and pay only for the features you intend to use—that is only buy the 20% you need; avoid the other 80%. Data center owners should consider defining the requirements for what they want to accomplish by moving to a DCIM system, before selecting the vendor or product. They should include in the requirements definition all organizational groups that will use the system. Also, in selecting the correct system, consider hiring a consultant who is not biased to any specific DCIM.

Most importantly, be realistic with expectations. For existing data centers, the move to a new DCIM system may take years to complete and longer to show a measurable ROI. It is critical to evaluate the training and support offered by the vendor and the flexibility of the system down the road.

Leading article image courtesy of Victorgrigas under a Creative Commons license

About the Author

infrastructure managementEhsaan Farsimadan is Director of Engineering at i3 Solutions Group. He previously worked as a technical consultant for the Uptime Institute, being responsible for data center design and facility Tier certifications. Before Uptime Institute, he served at Romonet as Head of Modelling and Customer Engineering, being responsible for the company’s client engineering and consulting engagements worldwide. He is a mechanical engineer with diverse industry experience. Ehsaan also previously worked as an M&E design consultant at Cundall, where he was responsible for developing data center concepts to scheme design in addition to leading the modeling team. He has developed specialities in data center predictive modeling and IT services, and he has good knowledge of electrical systems. Ehsaan is a chartered engineer accredited by the IMechE. In 2008, he obtained his doctorate (PhD) in mechanical engineering and also holds a bachelor’s degree in mechanical engineering with aeronautics. He has made significant contributions to the field of turbulence research and the data center industry through publication of a number of journal papers.

The post DCIM Part 1: What Data Center Operators Need appeared first on The Data Center Journal.


It’s a good time to be an IT professional, especially a data center professional. Today’s digital world is fueled by new technologies, enabled by the introduction of architectural changes, where skills like software development and IT management have become deeply intertwined. Consequently, technology providers are creating ever more sophisticated and advanced solutions to satisfy the needs of an interconnected and services-rich environment.

Many new drivers are changing the way we operate, such as rich media, collaboration environments and cloud-services delivery models. The massive influx of data from the growing Internet of Things is what really dictates the new requirements for the IT infrastructure: transaction times, transaction security, customer data protection, just-in-time offerings and more.

These new technologies have spurred new initiatives whose success depends on the data center’s infrastructure. Businesses are responding accordingly. Gartner reports that worldwide IT spending is on pace to total $3.8 trillion this year. In Computerworld’s annual forecast survey of IT executives, 43 percent of respondents said that they expect their IT budgets to increase, up from 36 percent in last year’s study. A significant amount of that money is going to the data center—just ask Google. The company spent more than $3.5 billion on “real estate purchases, production equipment, and data center construction” during the fourth quarter of 2014 and nearly $11 billion for the year.

In the recent past, data center budgets went primarily to upgrading or modernizing infrastructure, including building new data centers and implementing virtualization technologies, and the start of deploying workloads to cloud computing. Success was measured by improved energy efficiencies, lower TCO and, of course, good ROI.

Today, innovation is the driving force. Digital transformation initiatives, driven by business needs, are fueling the ongoing need to have highly scalable, always-available, secure data centers that these innovative initiatives can depend on. The goal for the modern IT data center is to be the elastic infrastructure to support business innovation initiatives (BIIs). Some call it IT as a service (ITaaS).

Below are four of the many IT projects that companies are investing in. These projects depend on the data center infrastructure: network, storage, computing and security services.

  • Big data analytics: Digital data volume is projected to reach 44 trillion gigabytes by 2020. Big data analytics enable the analysis and transformation of data into valuable insights that lead to new products and services.
  • Storage and server virtualization: This transition continues to be a major focus in order to meet high-availability and business-continuity requirements needed by the influx of business-driven IT projects.
  • Cloud services and cloud-based applications: This area continues to be a major emphasis for most companies. Everyone is already (or will be) working on, or supporting, some sort of private, hybrid or public cloud-based application.
  • Devices: Smart(er) devices generate massive volumes of data, and the opportunity is to capture it, manage it and turn it into useful information—in real time.

Although this mix of rapidly changing business needs and rich architectural offerings is generating opportunities, it is also placing a knowledge burden onto an evolving IT workforce, whose mandate is to become more and more focused on adding business value to their organization. What really matters is what we get from the new technology in practical terms, rather than how interesting or amazing the technology is. A good technology architect now must be able to clearly explain the value proposition of his or her solution.

This is an ongoing transformation that requires a shift in the way IT professionals plan and operate their careers. New technologies are shaping the workforce of the future, and the skills needed to meet the demand of our new digital age can be built on your current foundational skill set, regardless of whether you come from the IT world, the services world or the software-development world.

The IT workforce of the future will be an organized mix of all skill sets and will be capable of providing first-in-class support to professionals of the future, no matter which vertical they are attached to. It is likely that as you continue along your career path, you will find some technology areas in which you could stand to learn more. As usual, planning, designing, managing, implementing, troubleshooting, supporting and project managing apply; it’s a matter of adding expertise in these areas to support more-complex projects that you want to work on. Perhaps you have already been in the data center profession for several years and are well respected as a network administrator or data center engineer. If so, some advanced technology areas to add to your skill set include the following:

  • Network programmability or software-defined networks: Simplifying network architecture and operations
  • Cloud: Automation, orchestration, provisioning and cloud bursting (an application runs in a private cloud or data center and bursts into a public cloud when the demand for computing capacity spikes)
  • Fabric-based or unified computing: Coupled compute servers, storage and networks linked by high-bandwidth interconnects like 10 Gigabit Ethernet

Data center professionals already know that the demand for their services is strong. Recent job listings using a popular job-posting site for various data center job titles pulled up more than 13,000 available positions in the United States, United Kingdom, India and Australia combined.

Every region across the globe is experiencing strong demand for data center professionals, and the salaries for those positions reflect this demand. For those who choose a data center career, there is a wide variety of job roles or types, ranging from network administrator to senior network architect or senior data center architect, from associate to master or expert level. There are multiple management choices as well. It’s a career path rich with possibilities.

Leading article image courtesy of Dennis van Zuijlekom under a Creative Commons license

About the Author

data centerAntonella Corno, Manager of Product Strategy for Learning@Cisco, has 20+ years of experience in the IT industry, as part of a career spanning two continents (Europe and North America), in several leading IT companies—most recently at Cisco. Although she has been in technology R&D for most of her career, her interest has recently shifted to the training and certifications that are supporting and enabling the workforce of the future, and she is now responsible for the Application, Software and Cloud Team in the Product Strategy group of Learning@Cisco.

Antonella is a CCIE Emeritus, has been a speaker at several international conferences and holds a number of international patents.


The post Calling All Data Center Professionals appeared first on The Data Center Journal.


Looking to help make OpenStack a more accessible management platform for a much broader number of IT organizations, Mirantis announced it will apply $100 million in additional funding from investors toward making the open source framework much simpler to deploy and manage.

The post Intel Leads $100M Round for OpenStack Cloud Heavyweight Mirantis appeared first on Web Hosting Talk.


Looking to help make OpenStack a more accessible management platform for a much broader number of IT organizations, Mirantis announced it will apply $100 million in additional funding from investors toward making the open source framework much simpler to deploy and manage.

The post Intel Leads $100M Round for OpenStack Cloud Heavyweight Mirantis appeared first on Web Hosting Talk.


IBM’s new cloud-based IT services help quickly and securely integrate Apple’s Mac computers at scale with enterprise systems and applications.

The post IBM Rolls Out Enterprise Mac Services for Corporate IT appeared first on Web Hosting Talk.

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