Implementing energy efficient data centers

Implementing Energy Efficient
Data Centers

White Paper 114
Revision 1

by Neil Rasmussen

Contents
> Executive summary Click on a section to jump to it

Introduction 2
Electricity usage costs have become an increasing
fraction of the total cost of ownership (TCO) for data
centers. It is possible to dramatically reduce the The value of a watt 3
electrical consumption of typical data centers through
Energy consumption 5
appropriate design of the data center physical infra-
reduction in IT equipment
structure and through the design of the IT architecture.
This paper explains how to quantify the electricity Energy consumption 7
savings and provides examples of methods that can reduction in DCPI equipment
greatly reduce electrical power consumption.
Practical overall energy 9
consumption reductions

Conclusion 12

Resources 13

white papers are now part of the Schneider Electric white paper library
produced by Schneider Electric’s Data Center Science Center
DCSC@Schneider-Electric.com

Implementing Energy Efficient Data Centers

Introduction Electrical power usage is not a typical design criterion for data centers, nor is it effectively
managed as an expense. This is true despite the fact that the electrical power costs over the
life of a data center may exceed the costs of the electrical power system including the UPS,
and also may exceed the cost of the IT equipment. The reasons for this situation are as
follows:

• The billed electrical costs come after the charges are incurred and are not clearly linked
to any particular decisions or operating practices. Therefore they are viewed as inevita-
ble.
• Tools for modeling the electrical costs of data centers are not widely available and are
not commonly used during data center design.
• The billed electrical costs are often not within the responsibility or budget of the data
center operating group.
• The electrical bill for the data center may be included within a larger electrical bill and
may not be available separately.
• Decision makers are not provided sufficient information during planning and purchasing
decisions regarding the electrical cost consequences.

This paper will show that all of the above can and should be corrected, because substantial
financial savings are possible for typical users. The greatest advantage can be gained in the
design of new facilities, but some savings are possible for existing and evolving facilities as
well. Simple no-cost decisions made in the design of a new data center can result in
savings of 20- 50% of the electrical bill, and with systematic effort up to 90% of the
electrical bill can be avoided.

What is the cost of electrical power consumption?
A typical value for the cost of electrical power is $0.12 per kW hr. Given this cost, the annual
electrical cost per kW of IT load is approximately $1,000. Over the 10 year life of a typical
data center this translates to approximately $10,000 per kW of load.

As a general rule, approximately half of the energy used in a data center goes to the IT loads.
The other half goes to the data center physical infrastructure (DCPI) equipment including
power equipment. This means that for each kW of IT load the 10 year electricity cost is
approximately $20,000. For example, a 200 kW data center would have a 10 year electricity
cost of $4,000,000. This is a material cost for any organization and all IT professionals
should understand where this expense is going and that it is avoidable.

Where does the energy go?
Approximately half or less of the energy used in a data center goes to the IT loads. The other
half goes to the data center physical infrastructure (DCPI) equipment including power
equipment, cooling equipment, and lighting. Figure 1 shows the electrical power flow in a
typical high availability data center. Note that all the energy consumed by the data center
ends up as waste heat which is rejected outdoors into the atmosphere. Figure 1 is based on
a typical data center with 2N power and N+1 cooling equipment, operating at approximately
30% of rated capacity.

Schneider Electric – Data Center Science Center White Paper 114 Rev 1 2


Figure 1
Power flow diagram in a
typical data center

Related resource The above data center is said to be 47% efficient, based on the fraction of the input power
that actually goes to the IT load. For a more detailed understanding of where the power goes
White Paper 113
and how the different types of equipment contribute to the load, consult White Paper 113,
Electrical Efficiency Electrical Efficiency Modeling for Data Centers.
Modeling for Data Centers

Efficiency is a poor metric
Many discussions regarding electrical power consumption use the term “efficiency”. While
the underlying meaning of terms like “improving efficiency” are well understood, the technical
use of the term “efficiency” for quantitative assessment of data centers leads to confusion.
Discussions are much clearer when electrical power consumption (kW) is the metric used,
rather than efficiency metrics. For example, if two different devices in a data center are 50%
and 80% efficient, it is not clear how to combine their efficiencies into a single number that
relates to costs. In fact, the electrical costs would actually be dependent on the quantity of
power flowing through each device. Furthermore, some devices like computers or lighting
are zero percent efficient which is a confusing concept and conveys no quantitative informa-
tion regarding their electrical use.

In contrast, using electrical power consumption for a metric is simple and unambiguous. The
total electrical consumption is simply the sum of the consumption of all devices in the data
center. If one device uses $10 of electricity per month and another uses $20, it is a simple
matter to sum these values. Therefore, in this paper electrical power consumption will be the
quantitative term used instead of the more common but ambiguous term “efficiency”. A
complete discussion of the modeling of data center energy consumption is provided in White
paper 113.

The value Electrical power is sold in units of energy called kilowatt-hours (kW-hr), which is the amount
of energy delivered in one hour at a power level of 1000 Watts (1 kW). The distinction
of a watt between power and energy is very important for the economic analysis. Power capacity
costs are those associated with the systems that deliver energy and increase with the design
power level of the system. Examples of costs driven by power capacity are UPS costs,



generator costs, air conditioner costs, and power distribution equipment costs. Energy costs
are those associated with the electrical utility bill.

A key principle to understand is that reducing energy consumption can reduce the power
capacity related costs as well as the energy costs. That is, an implementation that saves
electricity in many cases can also save on the DCPI infrastructure costs, which are primarily
driven by the load power demand. A companion principle that is key to understand is that
there is a difference between reducing energy consumption temporarily and reducing energy
consumption permanently. Temporary savings like load shedding or server power manage-
ment reduces electricity costs but do not necessarily reduce the power rating of the DCPI
systems and the related DCPI infrastructure costs. Permanent or structural changes like high
efficiency servers or high efficiency UPS systems reduce both the electricity costs and the
infrastructure costs. These principles are illustrated in Table 1 along with example savings
values.
Table 1
The economic benefits of saving a kW or electrical consumption in a
typical high availability data center, comparing temporary and structural
consumption avoidance

Temporary Structural
consumption consumption Comments
avoidance avoided
Power High efficiency
Method of savings management servers
load shedding High efficiency
economizer UPS right-sizing

1-yr. electrical savings $960 $960 Assuming $0.12 per kW hour

10-yr. electrical
savings (IT) $9,600 $9,600 Typical design life of data center

10-yr. electrical Structural avoidance allows reduc-
savings (DCPI) $960 $13,760 tion in capacity-related electrical
consumption

DCPI CapEx savings Structural avoidance allows reduc-
$0 $13,300
tion in equipment capacity

Reduction in equipment reduces
DCPI OpEx savings $0 $6,600 operating expenses such as main-
tenance

Total 10-yr. savings
per kW $10,560 $43,260

In the example above, the data center is 2N redundant and operating at a typical 30% load.
Note that for a non redundant data center the savings would be greatly reduced, to about half
of the savings shown. Note also that in a typical situation not all of the installed power and
cooling capacity requirement can be avoided by a structural reduction, so the savings may be



further reduced. However, in general a reasonable estimate is that structural consumption
avoidance is worth twice as much as temporary avoidance.

Energy Clearly the primary driver of power consumption is the power draw of the IT equipment. IT
equipment power consumption directly contributes to the electrical bill, and it indirectly
consumption contributes by requiring various power and cooling equipment that also consume comparable
reduction in IT amounts of electricity. Therefore, all IT personnel should be concerned with controlling the
power consumption of IT equipment.
equipment
The methods for control of IT power consumption have historically been very weak. For
example, IT equipment vendors have not provided adequate information to allow users to
make decisions based on power usage. Users typically do not understand that they have IT
choices that can affect power consumption. However, the situation is improving and users
can take both operational and planning actions that will systematically reduce power con-
sumption.

The reduction of power consumption of IT systems consists of several approaches:
> Key point
• Operational actions: retiring systems, operating existing systems in an efficient manner,
The key point to understand and migrating to more energy efficient platforms
here is that there are two kinds
of energy consumption • Planning actions: virtualization, and standardization
reductions: Those that avoid
energy consumption, but do
not reduce power capacity Each of these will be discussed in turn.
requirements, and those that
also allow the reduction of
installed power capacity. We
will refer to those reductions in Operational: retiring IT systems
consumption that avoid energy
use without reducing installed Most data centers have old technology platforms that remain operational for archival or
power capacity as “temporary research purposes. In fact, most data centers actually have application servers which are
consumption avoidance” and
those that allow the reduction operating but have no users. It is useful to inventory these systems and create a retirement
of installed power capacity to plan. In many cases, systems can be taken off line and powered down, even if they are not
be “structural consumption physically retired.
avoidance”. Furthermore, for
data centers, a general rule is
that structural consumption A related opportunity exists where multiple old technology platforms can have their applica-
avoidance is worth approx- tions consolidated onto new servers, essentially reducing the total server count. This type of
imately twice as much as
temporary consumption
consolidation does not require virtualization, which is discussed later.
avoidance.
A power consumption reduction of up to 20% is possible in typical cases. Even if the floor
space is not recovered, the power capacity recovered can be very valuable as users
deploy higher density IT equipment.

Operational: operating existing systems in an efficient manner
Today, most new servers have power management features. That is, they are able to reduce
power consumption at times of reduced computational load. This was not true a few years
ago, when the power consumption of virtually all IT equipment was constant and independent
of computational load. Users should be aware of this change in IT technology, and be aware
of the status of the power management features on their IT systems. Where possible, power
management should be enabled on all devices with such capabilities. Note that many
equipment manufacturers supply equipment with these features disabled by default. This may
require upgrading applications to ensure that they take maximum advantage of the power
management features. Power management features reduce the total electrical usage but do
not reduce the power capacity requirement.



Operational: migration to energy efficient computing platforms
Migration to more electrically-efficient platforms is another effective strategy for reducing
power consumption. Most data centers have so-called “low density servers” that are 3-5
years old. Typically these servers draw the same or less power per server than today’s blade
servers and are physically much larger per server. Migration to modern blade servers from
legacy servers on a server-by-server basis typically does NOT reduce the total power
consumption and may even raise it. However, such migration will permit much higher packing
densities for servers. Blades do not create more heat than equivalent 1U servers, but they do
create heat in a smaller area which gives rise to heat removal problems that create the
perception that blades create excess heat.

When a new server deployment is planned, the use of blade servers as opposed to alterna-
tive server form factors will generally give a 20% reduction in power consumption. This is
because blade servers generally have higher-efficiency power supplies and share some
overhead functions such as fans. It is important to understand that selecting the blade form
factor reduces power consumption relative to other server form factors for newly deployed
equipment, but blades do not necessarily consume less power than older servers.

This discussion suggests that a server-by-server migration from existing server technologies
does not necessarily cause a significant reduction in power consumption. To determine the
potential to save power by migrating to blades on a server-by-server basis, the power
consumption of the existing server should be compared with the power consumption of any
proposed blade server. Furthermore, the performance of both servers should be compared so
as to arrive at a performance per watt metric. Today, major OEMs such as Dell, HP, and IBM
provide user configuration tools that accurately report actual power consumption for various
blade server configurations. To determine power consumption values for legacy servers, the
only realistic way is to measure example servers using a wattmeter. By comparing the values
obtained in this manner, the power savings due to a large scale server migration can be
estimated. Nevertheless, the following migration strategies are generally the most effective:

• Use a 2-way server or a single processor dual core server to replace 2 or more old
servers
• Use a blade based on a low-voltage or mid-voltage processor to replace an old server
• For servers with dedicated disk drives, use lower power enterprise class 2.5” drives
instead of 3.5”
• Use a single dual core processor server to replace a dual processor server
• Use a 2-way dual core server in place of a 4-way server

This discussion suggests that migration is not typically the most effective tool for power
consumption reduction. The major way that new server technologies can help reduce power
consumption is when consolidation of applications on servers is used to reduce the total
server count, or when servers are virtualized.

Planning: virtualization
Virtualization of servers results in a dramatic reduction of IT power requirements. Virtualiza-
tion almost always dramatically reduces the number of installed servers. The elimination of a
server is a structural consumption avoidance of approximately 200-400 W, depending on
technology. Therefore the electricity consumption avoided is approximately $380 per year per
server eliminated, and the total 10 year TCO cost saved by this structural avoidance is
approximately $7,680 per server eliminated. This savings is substantially greater than the
cost of the server itself.



Planning: standardization
Standardization on energy efficient servers is a very effective approach, even if virtualization
is not used. Today, blade servers are the most electrically efficient form of server. However,
the available blade types for a given blade server system can vary dramatically in perfor-
mance and power consumption. Often it is difficult to predict in advance the performance
requirement for a server-based application, so users often specify the highest available
performance at a substantial power consumption penalty.

When servers are virtualized, the strategy of using the highest performance server is
generally the best approach to minimize overall power consumption. However, when servers
are deployed application-by-application it can make sense to match the server performance
to the application requirements to save energy.

For users that standardize on a blade server system and deploy servers per application, the
option exists to standardize on two blades, a high performance / high power blade and a
lower performance / lower power blade. The range of power consumption can be greater than
two-to-one. A logical strategy is to deploy applications on the lower performance blade by
default and only move to a higher performance blade if the need is demonstrated. This is
facilitated by the ease of provisioning of blade servers. In this way, structural IT load con-
sumption avoidance of 10% or more is possible for a typical business data center.

Energy The reduction of energy consumption of DCPI equipment is accomplished using the following
techniques: right-sizing the DCPI system to the load, using efficient DCPI devices, and
consumption designing an energy-efficient system. Users may have some awareness of electrical efficien-
reduction in cy of DCPI devices during the purchasing process, but the fact is that the data that is
provided by manufacturers is typically not sufficient to determine actual energy consumption
DCPI equipment differences, and furthermore right-sizing and system design each have a much higher impact
on the electrical consumption than the selection of the DCPI devices.

Right-sizing
Of all of the techniques available to users, right-sizing the DCPI system to the load has the
most impact on DCPI electrical consumption. Most users do not understand that there are
fixed losses in the power and cooling systems that are present whether the IT load is present
or not, and that these losses are proportional to the overall power rating of the system. These
fixed losses are the dominant form of DCPI electrical consumption in typical installations. In
installations that have light IT loads, the fixed losses of the DCPI equipment commonly
exceed the IT load. Whenever the DCPI system is oversized, the fixed losses become a
larger percentage of the total electrical bill. For a typical system that is loaded at 30% of
rating, the electrical cost per kW of IT load is approximately $2,300 per kW per year. If the
system were right-sized to the load, the electrical cost per kW of IT load falls to approximately
$1,440 per kW per year which is a 38% savings in electrical costs as shown in Table 2.

Note that in addition to the electrical savings, right-sizing results in a savings of $1,400 per
kW per year of IT load in the DCPI capital and operating costs, which is almost as great as
the electricity savings. These are the potential savings for a specific example; actual savings
will vary and will be less for non-redundant systems

Right-sizing has the potential to eliminate up to 50% of the electrical bill in real-world
installations. The compelling economic advantage of right-sizing is a key reason why the
industry is moving toward modular, scalable DCPI solutions.



Table 2
The economic benefits of right-sizing a data
center showing the 10-year cost per kW

Baseline case Right-sized Comments

IT electricity $9,600 $9,600 Assuming $0.12 per kW hour

DCPI proportional loss $960 $960
Structural avoidance allows
DCPI fixed losses $12,800 $3,840 reduction in capacity-related
electrical consumption

DCPI capital cost Structural avoidance allows reduc-
$13,330 $4,000
tion in capacity equipment

Reduction in equipment reduces
DCPI operating cost $6,667 $2,000 operating expenses such as
maintenance

Total DCPI electrical
cost $13,760 $4,800 Total fixed and proportional losses

Total electrical cost
(DCPI + IT) $23,360 $14,400

Including DCPI power and cooling
Total 10-yr. TCO $43,360 $20,400 capacity and power consumption
expenses

Energy-efficient system design
Many users assume that the electrical consumption of a system is controlled by the efficiency
of the individual components, and therefore that the main approach to reducing power
consumption is to focus on the efficiency of the individual devices. This assumption is
seriously flawed. The system design has an enormous effect on the electrical consumption of
data centers, and two data centers comprised of the same devices may have considerably
different electrical bills. For this reason, the system design is even more important than the
selection of power and cooling devices in determining the efficiency of a data center.

Here are examples of system design issues that reduce the efficiency of data centers to a
much lower value than would be expected by summing the losses of the individual parts:

• Power distribution units and / or transformers operating well below their full load capaci-
ties.
• Air conditioners running with low output temperatures, continuously dehumidifying the
air which must then be continuously re-humidified using a humidifier.
• Air conditioners that are actually heating while others in the same room are cooling.
• Air conditioners forced to consume excessive power to drive air against high pressures
over long distances.



• Air conditioners operating with much lower return air temperature than the IT equipment
exhaust temperature, which causes them to operate at reduced efficiency and capacity.
• Cooling pumps which have their flow rate adjusted by throttling valves which dramatical-
ly reduces the pump efficiency.

Note that this list is mainly comprised of design problems related to air conditioning. In fact
most poor design practices that waste electrical power are related to air conditioning,
because the power systems architectures are more standardized and therefore less prone to
design-related errors.

The short list of problems above routinely cause data centers to draw twice as much DCPI
power as necessary. Furthermore, all of these problems are avoidable at little or no expense
by simple design decisions. There are two ways to avoid these problems:

1. Ensure the design has been fully engineered and tested to avoid the above problems,
including complex Computational Fluid Dynamics modeling and comprehensive com-
missioning testing, or
2. Obtain a complete DCPI system based on a standardized design, comprised of mod-
ules that have been pre-engineered, pre-tested, and specified to avoid the above
problems.

Due to extreme costs and variability of the first approach, the second of the above alterna-
tives will become the standard way that data centers are specified and acquired in the future.

Using efficient DCPI devices
Although the selection of DCPI devices such as power and cooling equipment has less effect
on the overall system electrical consumption than does IT architecture, DCPI right-sizing or
DCPI system design, device selection is nevertheless is an important element in designing a
power-efficient data center.

There is a substantial variation in the electrical losses between DCPI devices of the same
type operated under the same conditions. For example, in a December, 2005 paper by the
U.S. Electric Power Research Institute, it was found that different UPS systems operated at
30% of load rating varied in losses from 4% to 22%, which is a 500% variation. It is important
to note that this variation cannot be ascertained from the specification sheets for these
products. This paper and other white papers from Schneider Electric clearly demonstrate that
Related resource the electrical losses in real applications can only be correctly predicted if the appropriate
White Paper 108 models are used and that typical manufacturer’s data is inadequate to make quantitative
Making Large UPS Systems predictions of the electrical consumption of data centers. An example of the appropriate way
More Efficient to compare the electrical consumption of two DCPI devices is provided in White Paper 108,
Making Large UPS Systems More Efficient.

Practical overall This paper has demonstrated the magnitude of the electrical consumption problem and
suggested various strategies to reduce consumption. Combining the approaches, it is
energy possible to summarize the potential savings if a data center were optimized to reduce
consumption electrical consumption, as compared to a typical design.
reductions Table 3 summarizes 10 effective strategies that can be used to reduce electrical power
consumption along with a range of savings when compared to typical data centers. These
strategies are effective for new data centers, and some can be deployed immediately or over
time in existing data centers.



Table 3
Practical strategies for reducing electrical power consumption for
data centers, indicating range of achievable electrical savings

Savings Guidance Limitations

Right-size DCPI • Using a modular, scalable power and cooling architecture • For new designs and
10 – 30%
• Savings are greater for redundant systems some expansions

• Requires major IT
• Not technically a physical infrastructure solution but has radical process changes
impact • To achieve savings in
Virtualize servers 1 0– 40% • Involves consolidation of applications onto fewer servers, typically an existing facility
blade servers some power and
• Also frees up power and cooling capacity for expansion cooling devices may
need to be turned off
• Row-oriented cooling has higher efficiency for high density (White
Paper 130)
More efficient air • For new designs
• Shorter air paths require less fan power
conditioner architecture 7 – 15% • Benefits are limited to
• CRAC supply and return temperatures are higher, increasing
high density designs
efficiency, capacity, and preventing dehumidification thereby
greatly reducing humidification costs
• Many air conditioners offer economizer options
Economizer modes of air • This can offer substantial energy savings, depending on
• For new designs
conditioners 4 – 15% geographic location
• Difficult to retrofit
• Some data centers have air conditioners with economizer modes,
but economizer operation is disabled
• Floor layout has a large effect on the efficiency of the air
More efficient floor layout conditioning system • For new designs
5 – 12%
• Involves hot-aisle / cold-aisle arrangement with suitable air • Difficult to retrofit
conditioner locations (White Paper 122)
• New best-in-class UPS systems have 70% less losses than
More efficient power legacy UPS at typical loads
• For new designs or
equipment 4 – 10% • Light load efficiency is the key parameter, NOT the full load
retrofits
efficiency
• Don’t forget that UPS losses must be cooled, doubling their costs
• Many data centers have multiple air conditioners that actually fight
each other
Coordinate air • For any data center
• One may actually heat while another cools
conditioners 0 – 10% with multiple air
• One may dehumidify while another humidifies
conditioners
• The result is gross waste
• May require a professional assessment to diagnose
• Many vented tiles are located incorrectly in the average data • Only for data centers
Locate vented floor tiles center or the wrong number are installed using a raised floor
correctly 1 -6% • Correct locations are NOT intuitively obvious • Easy, but requires
• A professional assessment can ensure an optimal result expert guidance to
• Side benefit – reduced hot spots achieve best result
• Turn off some or all lights based on time of day or motion
Install energy efficient • Use more efficient lighting technology
• Most data centers can
lighting 1 – 3% • Don’t forget that lighting power also must be cooled, doubling the
benefit
cost
• Benefit is larger on low density or partly filled data centers
• Decrease server inlet temperature
• Also saves on energy by increasing the CRAC return air
Install blanking panels • For any data center,
1 – 2% temperature
old or new
• Cheap and easy with new snap-in blanking panels such as those
by Schneider Electric



Table 3 above provides a summary of some of the most powerful and practical tools to
reduce electrical power consumption of data centers. The energy consumption reduction
values were estimated using energy calculations based on White Paper 113 (referenced
earlier in this paper), applied to a range of data center designs. In addition to the items on
this list are other sophisticated IT architectural strategies that are mentioned earlier in this
paper.

Some of the savings described above can be built into the equipment provided by vendors,
but the majority is related to the system design and installation. Pre-engineered standardized
system designs are available from some vendors that have been optimized and verified for
high efficiency; consult your supplier. For users with existing facilities that seek to reduce the
electrical consumption, customers can attempt to implement the guidance provided above, or
some suppliers such as Schneider Electric offer a Data Center Energy Efficiency Assessment
Service that uses specialized tools and methods specifically designed for data centers.



Conclusion The cost of electricity for data centers is a substantial operating cost that can and should be
managed. A data center designed for reduced power consumption also saves other costs
such as capital and operating costs associated with power and cooling systems, as well as
saving space.

Electrical consumption of existing data centers can be reduced through various low cost
methods but primarily via migration to more energy efficient computing platforms. For new
data centers, there are additional options both in the IT architecture and in the DCPI architec-
ture that can gain much greater savings.

The electrical power consumption is typically shared evenly between the IT loads and DCPI
devices. Any rational approach to reduction in electrical usage must treat the combined IT /
DCPI design as a system in order to maximize the benefit.

Some equipment providers offer complete standardized data center designs specifically
engineered for efficiency, and energy efficiency audit services are available for users desiring
to reduce power consumption in existing data centers.

The cost savings opportunities have been shown to be very large yet the investment required
to achieve them is small or even zero in some cases, when compared with legacy approach-
es to data center design.

About the author
Neil Rasmussen is a Senior VP of Innovation for Schneider Electric. He establishes the
technology direction for the world’s largest R&D budget devoted to power, cooling, and rack
infrastructure for critical networks.

Neil holds 19 patents related to high-efficiency and high-density data center power and cooling
infrastructure, and has published over 50 white papers related to power and cooling systems,
many published in more than 10 languages, most recently with a focus on the improvement of
energy efficiency. He is an internationally recognized keynote speaker on the subject of high-
efficiency data centers. Neil is currently working to advance the science of high-efficiency,
high-density, scalable data center infrastructure solutions and is a principal architect of the APC
InfraStruXure system.

Prior to founding APC in 1981, Neil received his bachelors and masters degrees from MIT in
electrical engineering, where he did his thesis on the analysis of a 200MW power supply for a
tokamak fusion reactor. From 1979 to 1981 he worked at MIT Lincoln Laboratories on flywheel
energy storage systems and solar electric power systems.



Resources
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Making Large UPS Systems for Efficient
White Paper 108

Electrical Efficiency Modeling for Data Centers
White Paper 113

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