Harmonizing Global Metrics for Data Center Energy Efficiency
Global Taskforce Reaches Agreement Regarding Data Center Productivity
March 13, 2014
The data center has become an increasingly important part of most business operations in the twentyfirst century. With escalating demand and rising energy prices, it is essential for the owners andoperators of these mission-critical facilities to assess and improve data center performance using
energy efficiency and greenhouse gas (GHG) emission metrics. However, even with the global
presence of many companies, these metrics often are not applied consistently at a global level.
To address these inconsistencies, a group of global leaders has been meeting regularly to agree on
standard approaches and reporting conventions for key energy efficiency and GHG emission metrics.
These organizations are: the U.S. Department of Energy’s Save Energy Now and Federal Energy
Management Programs (March 2009 – October 2012); the U.S. Environmental Protection Agency’s
ENERGY STAR Program; the European Commission Joint Research Centre Data Centres Code of
Conduct; Japan’s Ministry of Economy, Trade and Industry; Japan’s Green IT Promotion Council;
and The Green Grid Association.

In February 2011, the Taskforce released a joint statement that highlighted the agreement reachedspecific to measurement protocols for the power usage effectiveness metric (PUE). The 2011
statement also included further goals and guiding principles for collaboration. In October 2012, theTaskforce released another joint statement providing measurement guidelines for three additional
metrics: green energy coefficient (GEC), energy reuse factor (ERF), and carbon usage effectiveness(CUE).
This current document, the last joint statement from the Taskforce, reflects agreements reached as ofMarch 6, 2014. It provides recommendations for quantifying data center energy productivity and an
update regarding data center productivity proxies, thus fulfilling this Taskforce’s current objectives.
Goal
Share global lessons and practices with an objective of arriving at a set of metrics, indices, andmeasurement protocols that can be formally endorsed or adopted by each participant organization to
improve data center energy efficiency and GHG emissions globally. This objective includes thefollowing specific goals:
1. Identify an initial set of metrics.
2. Define each metric.
3. Define the process for measurement of each metric.
4. Establish an ongoing dialog for the development of additional metrics.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 1

Desired Outcomes
The Taskforce has identified several desired outcomes for its activities.
Outcome (1) – Effective energy efficiency metrics that measure the actual IT work output of the data
center compared to its actual energy consumption. It is of note that in the process of defining
IT work output, the following interim measurements are being defined and/or validated:
Outcome (1.a) – IT – Measure the potential IT work output compared to expected energy
consumption, and measure the operational utilization of IT equipment.
Outcome (1.b) – Data center facility and infrastructure – Measure the data center
infrastructure and efficiency (i.e., power usage effectiveness or PUE).
Outcome (2) – Measure renewable energy technologies and reuse of energy to reduce carbon.
Guiding Principles
The Task Force recommends that the various data center efficiency metrics developed to address
Outcomes (1.a), (1.b), and (2) be viewed in a holistic, multi-parameter framework, such as a spider
chart, to help understand interactions among them. Viewing the metrics in a multi-parameter
framework helps owners and operators identify the effects of changes made to the data center
specific to the various efficiency metrics. (See Appendix F – Holistic Framework for more details on
this approach.) Along with the energy efficiency and GHG emission measurements and metrics, data
center owners and operators should keep absolute values in the forefront of all decision making.
Attempting energy efficiency or GHG emission programs should help to decrease the absoluteenergy use or GHG emissions per unit.
The Taskforce has finalized discussions and agreed on the following:
Outcome (1) – It is recommended that data centers begin to define attributes and measure data
center energy productivity (DCeP) according to the following: DCeP is an equation that quantifies useful work that a data center produces based on theamount of energy it consumes. DCeP is computed as useful work produced divided bytotal energy consumed by the data center. DCeP allows each user to define useful work as
applicable to the user’s business. This aspect allows each user to create a custom metricthat is meaningful for each user’s environment. Inconsistencies in data centercomparisons can develop with this approach, but the intent is that over time, through useof the metrics and communication, the industry can harmonize the attributes to minimize
inconsistency in comparisons. DCeP is discussed in greater detail in Appendix G – Data
Center Energy Productivity (DCeP)
Outcome (1.a) – This Taskforce has always aimed to recommend specific and comprehensive
metrics to address each outcome. In the case of Outcome (1.a), the Taskforce did not reachconsensus for a recommendation. However, the Taskforce has studied a wide range of proxies fordata center productivity and has narrowed the field to three proxies that each addresses thisoutcome in a different way. Appendix H lists the proxies studied and describes the approach andprocess employed by the Taskforce.
Harmonizing Global Metrics for Data Center Energy Efficiency

Page 2

Outcome (1.b) – It is recommended that data centers begin to measure PUE according to theseprinciples:

PUE using source energy is the preferred energy efficiency metric. PUE is ameasurement of the total energy of the data center divided by the IT energy consumption.



The industry should improve IT measurement capabilities to ultimately enable taking themeasurement directly at the IT load (e.g., servers, storage, networking, etc.). The
recommendation is to measure the IT energy at the output of the power distribution unit
(PDU). At a minimum, IT energy measurements should be measured at the output of theuninterruptible power supply (UPS).



For a data center, total energy measurement should include all energy sources at the pointof utility handoff. Total energy should include all cooling, lighting, and support
infrastructure, in addition to IT load.



Additional details on measurement guidelines have been added in Appendix A – Power
Usage Effectiveness (PUE) Measurement Details for PUE measurements, including
guidelines specific to equitable measurements of PUE with various designs: using
multiple energy inputs (including renewable energy), on-site generation, and reuse ofenergy.

Outcome (2) – It is recommended that data centers begin to measure GEC, ERF, and CUE
according to these principles:

GEC is a metric that quantifies the portion of a facility’s energy that comes from green
sources. GEC is computed as the green energy consumed by the data center (measured inkilowatt-hours or kWh) divided by the total energy consumed by the data center (kWh).
For the purposes of GEC, “green energy” is defined as any form of renewable energy forwhich the data center owns the rights to the green energy certificate or renewable energy
certificate, as defined by a local/regional authority. This definition is discussed in greater
detail in Appendix C – Green Energy Coefficient (GEC) Measurement Details. The “totalenergy consumed by the data center” is the total source energy, calculated identically tothe numerator of PUE.



ERF is a metric that identifies the portion of energy that is exported for reuse outside ofthe data center. ERF is computed as reuse energy divided by the total energy consumed
by the data center. Reuse energy is measured as it exits the data center control volume.
(For more, see Appendix D – Energy Reuse Factor (ERF) Measurement Details.) The“total energy consumed by the data center” is the total source energy, calculated
identically to the numerator of PUE.



CUE is a metric that enables an assessment of the total GHG emissions of a data center,
relative to its IT energy consumption. CUE is computed as the total carbon dioxide
emission equivalents (CO2eq) from the energy consumption of the facility divided by the
total IT energy consumption; for data centers with electricity as the only energy source,
this is mathematically equivalent to multiplying the PUE by the data center’s carbon

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 3emission factor (CEF). These calculation approaches are further defined in Appendix E –Carbon Usage Effectiveness (CUE) Measurement Details.
This guidance is meant to drive a common understanding of energy efficiency metrics. The
Taskforce has met in the U.S., Italy, and Japan and recently celebrated five years of collaboration.
With the release of this memo, the Task Force concludes its fulfillment of current desired objectives.
Participating Organizations
Each of the following organizations is represented on the Taskforce, which convened regularly toadvance the group’s progress.
For further details, please contact:
 U.S. Department of Energy Save Energy Now Program (March 2009 – October 2012):
http://www1.eere.energy.gov/industry/datacenters/contacts.html
 U.S. Environmental Protection Agency’s ENERGY STAR Program:
www.energystar.gov/datacenters European Commission – Joint Research Centre, Data Centres Code of Conduct:
http://iet.jrc.ec.europa.eu/energyefficiency/ict-codes-conduct/data-centres-energy-efficiency
 Japan’s Ministry of Economy, Trade and Industry: http://www.meti.go.jp/english/index.html
 Japan’s Green IT Promotion Council: http://www.greenit-pc.jp
 The Green Grid Association: gdcmetrics@lists.thegreengrid.org

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 4

List of Appendices
Appendix A – Power Usage Effectiveness (PUE) Measurement Details ..................................................... 6
Appendix B – Power Usage Effectiveness (PUE) Illustrative Calculation Examples ................................ 10
Appendix C – Green Energy Coefficient (GEC) Measurement Details ..................................................... 13
Appendix D – Energy Reuse Factor (ERF) Measurement Details.............................................................. 17
Appendix E – Carbon Usage Effectiveness (CUE) Measurement Details.................................................. 19
Appendix F – Holistic Framework.............................................................................................................. 24
Appendix G – Data Center Energy Productivity (DCeP) ........................................................................... 27
Appendix H – Data Center Productivity Proxies ........................................................................................ 28

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 5

Appendix A – Power Usage Effectiveness (PUE) Measurement Details
The seemingly simple concept of PUE requires additional detail to ensure that it is appliedconsistently across data centers. Consistent calculations of PUE facilitate communication of
performance among data center owners and operators.
PUE is computed as follows:
PUE = Total Data Center Source Energy
IT Source Energy
The following clarifications apply to the PUE calculation:Total Energy (Data Center Boundary) – Total Energy should be measured at the point ofutility hand off to the data center. This may include measurements of electricity, chilledwater, natural gas, fuel oil, or other purchases made from a utility. All measurements should
be made from the point where the energy is purchased. In the case where natural gas ispurchased to produce chilled water or electricity, measurement shall occur of the natural gas
purchase and not of the secondary energy it produces. Electricity from renewable energy
sources should be included in the total energy and assigned the same source factor as gridelectricity.
o Consistent Units – All energy shall be reported using the same units, and therecommended unit of measure shall be kWh. Standard thermal conversion factors
shall be used to convert to kWh (e.g. 1 kWh = 3.412 kBtu; 1 GJ = 278 kWh).



IT Energy – IT Energy shall be measured in kWh. The recommendation is to measure the IT
energy at the output of the PDU. At a minimum IT Energy should be measured at the outputof the UPS.



Source Energy – Source Energy is a means of assessing the total primary energy use or fuelconsumption at a data center. The purpose of source energy is to ensure that data centers thatpurchase different forms of energy (electricity, natural gas, chilled water) can compare their
total consumption equitably. For example, if one data center purchases chilled water from a
local utility company while another data center uses electricity to produce chilled water
onsite, a factor is required to put the two utility bills in common units for comparison. Thisfactor is called a source energy factor, and it is a factor to reflect the total fuel consumption.
Because the majority of data centers operate with 100% electricity, this global group
recommends source energy factors that are weighted with respect to electricity. In otherwords, after source factors are developed for each fuel, they are each expressed relative to thesource factor for electricity. This convention allows for any electric purchases to be
multiplied by a factor of one. Purchases of other fuels are multiplied by their respective
factors before being added to the total.
Weighted energy for each energy type = (Annual energy use * source energy weighting
factor)Harmonizing Global Metrics for Data Center Energy Efficiency

Page 6

The source energy factor is inherently a regional factor because the amount of fuel needed to
produce one unit of electricity (or chilled water) will depend on the predominant method ofelectric (or chilled water) generation in the specific country. Some countries have publishedvalues for these conversion factors (such as the EPA ENERGY STAR Program). However,to enable a fair global comparison, this global group recommends that standard, global
factors be applied. Global factors represent average values of factors from different regions.
While the global factors are recommended for comparisons across different regions of theworld, regional factors may be applied for greater accuracy of comparison within a specificregion.
The global average source energy weighting factors shall be:
Energy Type
Weighting Factor
Electricity
1.0
Natural gas
0.35
Fuel oil
0.35
Other fuels
0.35
District chilled water
0.4
District hot water
0.4
District steam
0.4
Table 1: Global average source energy weighting factors
To express Total Source Energy, each fuel should first be converted into a single common
unit (kWh); then each fuel should be multiplied by its weighting factor; finally, the sourceenergy for all fuels can be summed together.


On-Site Electric Generation – To enable equitable PUE calculations in data center designs
that operate on-site electric generation two topics need to be explained:o IT Source Energy Conversion Factor
 To ensure the PUE calculations for a design including on-site electric
generation and a design excluding on-site electric generation are equitable,
the taskforce has introduced the IT Source Energy Factor. The IT Source
Energy Factor enables the data center owner / operator to include theefficiencies of generating chilled water (as in all calculations for PUE), but
exclude the efficiencies of generating electricity (as in all calculations for
PUE). PUE is not a metric to identify the efficiencies of how electricity isbrought to the data center, it is a metric to identify how efficient theelectricity is used from the data center control volume to the IT Equipment.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 7



Example A: Data center with electric and natural gas (Co-generation) purchaseo Cogeneration Input Fuel Assumption
 This taskforce has adopted the assumption that 67% of a cogeneration plants’
input fuel is allocated to the primary form of generation (electricity in theprovided example) and respectively 33% of the primary input fuel is allocatedto the secondary form of generation (chilled water in the provided example).
 This assumption is based on a similar steam based cogeneration study fromthe U.S. Environmental Protection Agency’s Energy Star Program.
Differences do exist between the two scenarios, but to solidify themethodology of calculating PUE and to drive consistent and equitablecalculations for all design types, this taskforce is proceeding with therecommendation to adopt this approach. As further data specific tocogeneration examples are provided, the data will be taken into consideration.
Provided there is a material change in calculations, the taskforce will consider
updating the recommendations specific to this assumption.
 Stakeholder feedback is welcome and encouraged to continue the adoption,
consistent communication, and equitable calculations for PUE in an effort toimprove energy efficiency.
Energy Re-use – Energy that is re-used shall not be subtracted from the total energy use of
the data center.
o If the energy is reused within the datacenter, the value of the reused energy will
already be contained within the PUE calculation and needs no special consideration.
An example will be reusing waste heat to warm a battery space in a cool climate. The
PUE would generally be lower because the HVAC system does not have to add as

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 8much energy to heat that battery space, thereby reducing the PUE when energy ismeasured and PUE calculated.
o If the energy is reused outside the data center (e.g. re-using heat to warm a lab that is
not part of the data center), the PUE of the data center is not affected. While the effort
to conserve energy is laudable, it cannot be accounted for in the calculation of thedata center PUE because it does not lower the actual total energy need of the data
center.


Measuring PUE in a Mixed-use Buildingo The definition of a Mixed-use Building is a facility that has a data center and services
not associated with the data center. As an example a Sales team or Marketing teamare also housed within a building, but have no direct support responsibilities for thedata center.
o The method for calculating PUE in a mixed use building is the same method for
calculating PUE in a dedicated data center except for some ancillary energy loads thatcan be excluded*: Energy required for shared support spaces: (ancillary energy loads)
 Shared offices
 Shared labs
 Shared cubicles
 Shared conference rooms Shared elevators
 Shared lobbies
 Shared Kitchens / break rooms* When excluding any ancillary energy loads this should be declared whenreporting PUE.
 The shared elevator is one example to illustrate the rationale for the above
noted exceptions. A data center can be a small portion of one floor located on
one level of a multistory building. The data center is located on the 5th floorof a 10 story building. The 5th floor also is also mixed-use with manybusiness services and a data center. The effort to implement energy
measurements on the shared elevator and the effort to try to quantify how
much of that energy should be allocated to the data center traffic on the 5th
floor is believed to be a point of diminishing returns. The proportional load
for the elevator is insignificant in the PUE calculation.
 Energy consumption of all dedicated space is to be included. As an example
if there are offices or cubicles that are dedicated to the data center operations,the energy consumption should be included. Provided the above direction isfollowed, PUE reporting of data centers within mixed-use buildings follow
the same nomenclature as PUE reporting of dedicated data centers.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 9Appendix B – Power Usage Effectiveness (PUE) Illustrative Calculation Examples
The following illustrative examples have been provided to demonstrate a few data center designs andthe corresponding approach to calculate PUE by the above recommendations:

Example B – Data center with all electric purchaseHarmonizing Global Metrics for Data Center Energy Efficiency

Page 10



Example C – Data center with electric and chilled water purchaseExample D – Data center with natural gas purchaseHarmonizing Global Metrics for Data Center Energy Efficiency

Page 11

Example E – Data center with electric and natural gas purchaseHarmonizing Global Metrics for Data Center Energy Efficiency

Page 12

Appendix C – Green Energy Coefficient (GEC) Measurement Details
Conceptually, GEC is a simple metric: the percent of energy that is green.
Complexity arises because there are regional and local differences in the definition of renewable /green energy. These regional differences may occur due to the different types of renewable energythat are prevalent, and due to the market for green energy products in each country. This task force
does not attempt to reconcile regional differences.
Rather, this task force recognizes green energy as any energy for which the data center owns thelegal right to the environmental attributes of green/renewable generation. These legal rights are
regionally recognized as Green Energy Certificates, Renewable Energy Certificates, and other similar
products. Due to regional differences, this Taskforce recommends all public reporting of GEC
include the source issuer of the certificates. Any energy for which the data center owns these rights is
considered green for the purposes of this metric. This would include retired Green Energy
Certificates, which are no longer a commodity to be traded and are possessions of the last owner or
the green certificate system administrator.
The following table contains a number of authorities recognized by this Task Force as defining or
issuing the equivalent of a Green Energy Certificate as described herein. This list is notcomprehensive and as other regional bodies emerge that issue and track green certificates, thatenergy should also be counted as green for the purposes of this metric.
Region Regional Authority / Issuer(s)Link
http://www.aibnet.org/portal/page/portal/AIB_HOME/EECS/Fact_Sheets (See Fact Sheet 4)
http://www.decc.gov.uk/en/content/cms/meeting_energy/renewable_ener/renew_obs/renew_obs.aspx

E.U.

Association of Issuing Bodies
Department of Energy andClimate Change

Japan

The Green Energy Certification
Centerhttp://eneken.ieej.or.jp/greenpower/eng/index.htm

U.S.A

U.S. EPA Green PowerPartnershipGreen-e

http://www.epa.gov/greenpower
http://www.green-e.org/

Table 2: Authorities defining or issuing the equivalent of a Green Energy Certificate
Given this definition of green energy, GEC is computed as follows:
GEC=

Green energy used by the data center
Total Data Center Source Energy

GEC has a maximum value of 1.0, indicating 100% of the total energy used by the data center isgreen energy as defined above. Note that for the purposes of this calculation, the total source energy
Harmonizing Global Metrics for Data Center Energy Efficiency

Page 13consumed at the data center is identical to the numerator of PUE. As with PUE, all energy shall bereported using the same units, and the recommended unit of measure shall be kWh. Standard thermal
conversion factors shall be used to convert to kWh (e.g. 1 kWh = 3.412 kBtu; 1 GJ = 278 kWh).
Finally, because the definition of green energy is based on legal ownership of the rights to theenvironmental benefits it is important to clarify that the location of energy source does not change
the calculation of the GEC. For example, a data center may have a solar panel on its roof togenerate power; if it sells the Green Energy Certificates associated with this power, then it cannot
claim any of that electricity as green. Conversely, a data center that receives electricity entirely
from a coal-fired plant may purchase Green Energy Certificates to off-set the entire electric use.
These certificates would count as Green Energy.
These concepts are illustrated in several examples, as follows.


Example F: Grid energy purchased without Green Energy Certificates: GEC = 0.00.

Power Station

Grid Power
distribution

Utility meter

No Purchased Green Energy Certificates

Example G: Green Energy Certificates purchased and retired for 20% of
the energy used by the DC: GEC = 0.20

Power Station

Grid Power
distribution

Utility meter

DC Purchased Green Energy Certificates for
20% of energy used.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 14



Example H: Green Energy Certificates for locally generated green
energy are retired by the DC: GEC = 0.10.

90 GWhPower Station

Grid Power
distribution

Utility meter

10 GWhDirect Gen
Meter
Green Energy Certificates NOT sold to 3 rd party.
DC has the right to use the Green Energy.
[No additional Certificates purchased.]
Renewable Energy
Generation



Example I: Locally generated energy sold to the utility, but Green
Energy Certificates retired by the DC: GEC = 0.20.

100 GWhPower Station

Grid Power
distribution

Utility meter

Green Energy Certificates
NOT sold to 3rd party.

20 GWhGreen
EnergyCertificate
Renewable Energy
Generation

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 15



Example J: The DC can retire Green Energy Certificates only for the energy it uses: GEC =1.00. Note that of the 130 GWh generated by renewable sources, the data center could sell 30
GWh worth of Green Energy Certificates without impacting its GEC of 1.00.

90 GWhPower Station

Grid Power
distribution

Utility meter

10 GWh10 GWh120 GWh

Direct Gen
Meter

Green Energy Certificates NOT sold to 3rd party.

Green
EnergyCertificate

Renewable Energy
Generation

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 16

Appendix D – Energy Reuse Factor (ERF) Measurement Details
Increasingly in data centers, the energy from the data center is being reused in other parts of the
facility or campus with beneficial results. The benefits of energy reused within the data center are
captured by the PUE metric. Therefore the purpose of ERF is to incent reuse of energy outside of thedata center rather than rejection of energy. ERF is a metric that identifies the portion of energy that isexported for reuse outside of the data center. For example, reuse energy includes energy that isexported outside of the data center to another area within a mixed-use building or to another facility.
The basic equation for ERF is as follows:
ERF = Reuse energy outside of the data center
Total Data Center Source Energy
ERF will range from 0.0 to 1.0. The value at 0.0 means no energy is exported from the data center forreuse, while a value of 1.0 means all of the energy brought into the data center is reused outside ofthe data center.
With ERF, the concept of a control volume must be used. In simplest terms, the control volume is theimaginary boundary around the data center and its infrastructure support areas, such that whenenergy crosses this boundary, it needs to be accounted for. In Example K, the control volume isindicated by the dashed line.
To compute ERF, any energy used by the data center that leaves the control volume for use
elsewhere, is considered Reuse Energy. In Example K, the Reuse energy is denoted by (G), andshould not be confused with the reject energy (I). Total energy consumed by the data center is
identical to total energy as defined in PUE and includes all energy sources that enter the control
volume as denoted by (A). The source of the reuse energy must be known to enable equitablecalculations. The numerator is the re-use energy, also expressed in source energy. This captures allenergy that leaves the control volume. If energy leaves the control volume in the same form as itentered (e.g. extra chilled water that is passed along to another building), then the standard sourceenergy factors from Table 1 are used. However, if the energy that is reused is of a different form
because it was created within the data center, then the actual COP or efficiency of production must
be used to determine the correct factor.
Finally, while not captured within ERF it is important to consider the intrinsic value of the reuseenergy (e.g. hot air versus hot water) and how the energy is actually re-used after it leaves the control
volume. One should ensure that the process overhead to reuse the energy does not exceed theequivalent direct use of an energy source. The net total energy consumption should be less whenreusing energy.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 17



Example K: ERF showing the data center control volume.



Example L: ERF showing Electric & Chilled Water Purchase – Reuse from Chilled Water.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 18

Appendix E – Carbon Usage Effectiveness (CUE) Measurement Details
CUE is a metric to understand the carbon dioxide emission equivalents (CO2eq) from energy
associated with a data center. There are many commonly accepted international protocols thatprovide direction for quantifying total emissions of any type of facility. For a data center inparticular, CUE is a recommended metric because it evaluates the total GHG relative to the IT energy
consumption. Whereas an office building may be most interested in GHG per square foot, and an
automobile manufacturing plant may be interested in GHG per automobile produced, for the data
center, this Task Force recommends that GHG is evaluated relative to the IT energy consumption,using CUE.
Consistent with the desired outcomes of this Taskforce the scope of CUE includes the emissions
from energy consumption and excludes the emissions generated in the manufacturing of the IT
equipment, its subsequent shipping to the data center, the construction of the data center, etc. The
Taskforce considers the full life-cycle to be important to the overall sustainability of the industry but,for practical considerations, they are excluded from this metric. For now, CUE is specifically limited
to Scope 1 and Scope 2 emissions. For more on the definitions of Scope 1 and Scope 2 emissions,refer to: World Resources Institute and World Business Council for Sustainable Development(WBCSD), A Corporate Accounting and Reporting Standard (2004)
http://www.ghgprotocol.org/files/ghgp/public/ghg-protocol-revised.pdf, page 25.
To accomplish this complete inventory of emissions from all energy consumption, a data center must
include emissions from all energy sources (Note purchased carbon off-sets, green energy certificates,
and renewable energy certificates are excluded from CUE):
 Electricity
 District heat (chilled water, hot water, steam) Primary energy sources (natural gas, diesel, bio-gas, etc.)
 Renewable energy produced onsite
o If renewable energy is produced on-site (e.g. through solar or wind power), then theemissions of this energy depend on whether or not the Data Center owns the rights to
the green energy certificate or renewable energy certificate, as defined by alocal/regional authority. If these rights have been sold, then the emissions of thisenergy is equivalent to what the emissions would be with a conventional utility
purchase (e.g. grid-electric purchase). If the rights have not been sold, then theemissions for the renewable source are obtained from the references in Table 3.
For Data Centers with electricity as the only energy source, the recommended equation for CUE is:
CUE = CEF X PUE
Where:
 CEF is the Carbon Dioxide Emission Factor (kgCO2eq/kWh) of the site. For all electric data
centers the CEF is the single value obtained from Table 3.
 PUE is described in Appendix A.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 19

For Data Centers with multiple energy sources the recommended equation for CUE is:
Total C02 emissions caused by the Total Data Center Energy/ IT Equipment Energy
Where:
 Each type of energy source total C02 emissions are addedo IT Equipment Energy is defined as the Total kWh delivered to the IT Equipment
The units of the CUE metric are kilograms of carbon dioxide emission equivalents (kgCO2eq) per
kilowatt-hour (kWh) of IT energy consumption. CUE has an ideal value of 0.0, indicating that no
carbon use is associated with the data center’s operations.
Carbon emission factors should be obtained from the government’s published data for the region ofoperation for that year. All reports for CUE should disclose the source of the CEF used in thecalculation.
RegionE.U.

Japan
U.S.A.



Recommended Carbon Emission Data for Procured Energy
http://re.jrc.ec.europa.eu/energyefficiency/covenantofmayors/seap_guidelines_en-2.pdf
http://archive.defra.gov.uk/environment/business/reporting/pdf/100805-guidelines-ghgconversion-factors.pdf
http://ghg-santeikohyo.env.go.jp/files/calc/itiran.pdf
http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007V1_1_year05_Summar
yTables.pdf
http://www.energystar.gov/ia/business/evaluate_performance/Emissions_Support
ing_Doc.pdf?ac4e-840b
Table 3: Recommended Carbon Emission Data for Procured Energy

Example M: CUE with all Electric Energy Purchase Data Center

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 20

CEF and CUE:
 In this example, let’s use US WECC CA as electric source to calculate CUE
 We can use EPA’s reference information to get CEF from:http://www.energystar.gov/ia/business/evaluate_performance/Emissions_Supporting_Doc.pd
f?d258-68cc
 Indirect Greenhouse Gas Emission Factors – Purchased Electricity













This WECC CA shows CEF as 90.8653 KgCO2e/MBtu.
o 1Btu= 0.293Wh
CEF will be 90.8653 / 0.293 = 310.12048 KgCO2e/MWh = 0.31012 KgCO2e/KWh
KgCO2e/KWh for Electricity = 0.31012
Electricity kWh: 1,633,333 kWh
Total Annual Energy: 1,633,333 kWh
Total Annual Emissions from Electricity = 1,633,333 *.31012 = 506,529.23
Total Annual Emissions from energy = 506,529.23
CEF = the total annual emissions from all sources divided by the total annual energy.
CEF = 506,529.23 / 1,633,333 = .31012
PUE = 1.63 in this example
CUE, as a function of CEF*PUE = 1.63*.31012 = .51

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 21

Example N: CUE at a mixed energy purchase Data Center. (Electricity & Natural Gas)

CUE calculation:









In this example, let’s use US WECC CA as electric source to calculate CUE
We can use EPA’s reference information to get CEF from:http://www.energystar.gov/ia/business/evaluate_performance/Emissions_Supporting_Doc.pd
f?d258-68cc
Indirect Greenhouse Gas Emission Factors – Purchased Electricity

This WECC CA shows CEF as 90.8653 KgCO2e/MBtu.
1Btu= 0.293Wh
Then, Electric part of CEF will be 90.8653 / 0.293 = 310.12048 KgCO2e/MWh = 0.31012
KgCO2e/KWh

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 22















For the Natural Gas part, CEF can be found at this table of the same EPA link, as
53.200036KgCO2e/MBtu.
Direct Greenhouse Gas Emission Factors

Btu is required to covert Wh, 1Btu= 0.293Wh
Natural Gas part of CEF is 53.200036 / 0.293 = 181.5701 KgCO2e/MWh =0.18157KgCO2e/KWh
KgCO2e/KWh for Electricity = 0.31012
KgCO2e/KWh for Natural Gas = 0.18157
Electricity kWh: 843,750 kWh
Natural Gas kWh: 1,587,000 kWh
Total Annual Emissions from Electricity = 843,750*.31012 = 261,663.75
Total Annual Emissions from NG = 1,587,000*.18157 = 288,151.59
Total Annual Emissions from energy = 261,663.75 + 288,151.59 = 549,815.34
CUE, by definition, Total C02 emissions caused by the Total Data Center Energy/ Total kWhdelivered to the IT Equipment = 549,815.34/ (1,000,000) = .55

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 23

Appendix F – Holistic Framework
A holistic framework approach enables awareness of the effect of changes made to the data center
specific to the various efficiency metrics. For example, one improvement at a data center that istargeted to improve a specific performance metric could have the result of making another metriclook worse. A holistic framework helps the operator keep in mind the effects on all metrics
simultaneously.
The Desired Outcomes section lists following three aspects of energy efficiency (or reduction ofcarbon emissions) of a data center. A Holistic framework is a way to grasp multiple metrics forenergy efficiency of a data center collectively:
Outcome (1.a) – IT – Measure the potential IT work output compared to expected energy
consumption; and measure operational utilization of IT Equipment.
Outcome (1.b) – Data center facility and infrastructure – Measure the data center
infrastructure and efficiency (Power Usage Effectiveness - PUE)
Outcome (2) – Measure renewable energy technologies and re-use of energy to reduce carbon.
To help understand and improve energy efficiency and GHG Emissions of data centers, it is useful toview all these aspects in a single chart. A spider or radar chart such as Figure 1 is a way to view thevarious metrics in a holistic framework. In the spider chart, metrics for all the Desired Outcomes(1.a), (1.b) and (2) are distributed to its axes to evaluate total energy efficiency of a data center.
As a holistic framework, each Desired Outcome [as listed at above; (1.a), (1.b) and (2)] should haveat least one, or more where applicable, metric in the spider chart. At this time the Taskforce has notprovided a recommendation for Desired Outcome 1.a. Once agreement is reached and arecommendation provided, the recommended metric(s) should be included into the holistic
framework. The number of metrics (axes) may vary depending on the selection of metrics chosen
by the data center operator. Figures 2 and Figures 3 shows variations of the spider chart with 4 and 5axes, respectively. In Figure 2, two metrics are adopted to Outcome 1.a. In Figure 3, three metrics are
adopted to Outcome 2. These metrics evaluate different aspects of Outcome 1a and Outcome 2.
Once the metrics are selected, the operator will also need to identify a start and end point for eachaxis. In some cases, there are theoretical maximum and minimum values (e.g. GEC can only range
from 0.0 to 1.0). In other cases, there are not clean maximum values (e.g. there is no maximum for
PUE). Therefore, the axis ends will have to be established based on target values or other estimates.
The selection of metrics and the selection of the axis end points both establish an implicit weighting
among the metrics, therefore care should be taken during this process.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 24

Outcome 1a[Axis 1] Outcome 1a
Measure operational utilization of IT
Equipment and the potential IT work
output compared to expectedenergy consumption[Axis 3] Outcome 2
Measure renewableenergy technologies andre-use of energy to
reduce carbon

Measured

Outcome 2

Outcome 1b[Axis 2] Outcome 1b
Measure the data center
infrastructure and theefficiency

Figure 1: A possible expression of “Holistic Framework”
Outcome 1a-1

Outcome 2

Outcome 1a

Outcome
1a-2

Outcome 2-3

OutcomeMe
Measured

Outcome 1b

Outcome 2-2(a) A spider chart with 4 axes

Outcome 2-1(b) A spider chart with 5 axes

Figure 2: Possible variations of a spider chart expression

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 25

Outcome 1a-1

utcome 2

Outcome 1a

Outcome
1a-2

Outcome 2-3

Outcome 1b

Measured
Outcome 1b(a) A spider chart with 4 axes

Outcome 2-2

Outcome 2-1(b) A spider chart with 5 axes

Figure 3: Possible variations of a spider chart expression
The spider chart approach offers several benefits, by providing a combined visualization of all
metrics. In particular, by looking at a spider chart, a data center operator can see which metric isworst – this may be the metric with the greatest area for improvement. Another benefit is that the
spider chart lets an operator visualize the effects of an efficiency improvement on all metrics. The
goal is to move all metrics in the same direction toward increased efficiencies. Therefore, it would be
important after investing in changes at a data center to review the spider chart and confirm that all
metrics have improved. Finally, the spider chart can be used not only to plot current measured
performance, but also to plot target performance, so that progress towards a target can be tracked forall metrics at once.

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 26

Appendix G – Data Center Energy Productivity (DCeP)
Data center energy productivity (DCeP) is an equation that quantifies useful work that a data center
produces based on the amount of energy it consumes. “Useful work” is a sum of tasks that arecompleted in a specified period of time, where each task is weighted based on a specified relativevalue. In this way, more-valued tasks receive greater weighting in the calculation of useful work, andless-valued tasks receive lesser weighting. DCeP allows each user to define useful work and theweighting for various forms of useful work (if measuring more than one type) that apply to thatuser’s business. For example, a retail business may use the number of sales as its measure for useful
work, an online search company may use the number of searches completed, and so on. The
definitions can get as granular as necessary for the entity using the equation: web pages served,
database transactions executed, emails served, etc.
Mathematically, DCeP can be expressed as:
DCeP = Useful Work Produced / Total Data Center Source Energy Consumed Producing this Work
Where:

Useful Work Produced =M is the number of tasks initiated during the assessment window
Vi is a normalization factor that allows the tasks to be summed numerically
Ti = 1 if task i completes during the assessment window, and Ti = 0 otherwise
Ui(t,T) is a time-based utility function for each task
t is elapsed time from initiation to completion of the task
T is the absolute time of completion of the task
The DCeP denominator, Total Data Center Source Energy Consumed Producing this Work,is identical to the PUE numerator.
Note that Useful Work Produced is defined as the sum over i of all tasks 1 through M initiated within
the assessment window multiplied by a time-based utility function Ui(t,T). The factor Vi assigns anormalized value to each task so that they may be algebraically summed. Ti eliminates all tasks thatare initiated either prior to the assessment window or within the window but do not complete.
For further details, see The Green Grid Association published material:

http://www.thegreengrid.org/Global/Content/white-papers/Framework-for-Data-CenterEnergy-Productivity

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 27

Appendix H – Data Center Productivity Proxies
While DCeP represents data center productivity in terms that are meaningful to a specific data center,a productivity proxy is a relatively simple metric that aims to approximate data center productivity
generically.
A productivity proxy should address Outcome (1.a):
Outcome (1.a) – IT – Measure the potential IT work output compared to expected energy
consumption, and measure the operational utilization of IT equipment.
Although the Taskforce did not reach consensus for a recommendation, the following is the approach
the Taskforce followed to address Outcome (1.a). Through industry research and group discussion,
the Taskforce began reviewing the following metrics:










Useful work self-assessment & reporting
Data center energy productivity (DCeP) by energy checker
Data center energy productivity (DCeP) sample load
Network traffic (bits) per kilowatt-hour
Weighted CPU utilization – SPECint_rate
Weighted CPU utilization – SPECpower
Compute units per second (CUPS)
Operating system workload efficiency
IT equipment energy efficiency (ITEE) x IT equipment utilization (ITEU)

Independently, members of the Taskforce undertook structured experiments to assess the various
proxies, and they performed industry trials. Based on a general discussion about accuracy and ease ofuse, the Taskforce decided to have more detailed discussions on three metrics: network traffic (bits)
per kilowatt-hour, weighted CPU utilization – SPECint_rate, and IT equipment energy efficiency x
IT equipment utilization.
Network traffic (bits) per kilowatt-hour is calculated as:Outbound bits / data center energy
Weighted CPU utilization – SPECint_rate is calculated as:(SPECint_rate * CPU utilization) / data center energy
IT equipment energy efficiency x IT equipment utilization is calculated as:(Benchmark performance / rated power) x (IT energy / IT rated energy)
For further details regarding the three metrics reviewed by this Taskforce, see the associatedpublished material:
CPU SPECint_rate: http://www.thegreengrid.org/library-and-tools
ITEE x ITEU: http://home.jeita.or.jp/greenit-pc/e/topics/release/100316_e.html
Network (bits): http://www.thegreengrid.org/library-and-tools
Harmonizing Global Metrics for Data Center Energy Efficiency

Page 28

These three metrics were discussed further within industry forums, Taskforce technical review, andadditional experiments. The three metrics were then evaluated through the following specificevaluation criteria and finally through a facilitated pros and cons review.
Evaluation Criteria
The following criteria were used to analyze the three proxies. The list is included below to indicate
the level of due diligence conducted by the Taskforce.
 Operational measurement: Is the proxy based on a measurement taken during data center
operations?
 Track actual productivity: Does the proxy positively correlate with actual productivity in thedata center?
 Representative of desired Outcome (1.a): Does the proxy accurately represent Outcome (1.a)
as defined by the Taskforce?
 Relevance: Does the proxy provide relevant and useful information to the target audience?
 Comparable: Does the proxy allow normalized comparison between data centers, such as
within a single company or market sector or across market sectors?
 Time stable: Can the proxy be used to compare past performance with present performance?
 Perverse incentives: Does the proxy definition account for perverse incentives it could
create? How impactful are those perverse incentives?
 Incent energy efficiency: Does the proxy provide an incentive to reduce energy consumption
for the same work load?
 Ease of use: Is the proxy easy to implement and use?
Pros and ConsThe evaluation of the proxies against the above criteria revealed a number of pros and cons
associated with each proxy. The Taskforce identified the following list of major pros and cons.

MetricPros
CPU
1. Correlates well with actual work
SPECint_rate 2. Easy to understand3. Using the well-known benchmark
enables easier comparison
ITEE x ITEU 1. Relatively easy to calculate
2. Includes storage and networking
3. Benchmark data is available

Network(bits)

1. Easy to understand and calculate
2. Easy to obtain information
3. Correlation to work and ease ofuse is goodCons
1. Does not include storage andnetworking
2. Difficult to collect required data
(CPU utilization)
3. Does not include 100% of servers
1. Concerns with implicit weighting
factor for server, storage, and network
2. Concern with storage and network
productivity measure (capacity)
3. The metric is heavily reliant on
catalog data rather than measured values
1. Depending on the data center type,the relevance of the metric can be low(such as for a research data center)
2. Too simple, less focus on how muchwork is complete inside the data center3. Comparison within market sector isdifficult

Harmonizing Global Metrics for Data Center Energy Efficiency

Page 29