DPPE: Holistic Framework for Data Center Energy
Efficiency- KPIs for Infrastructure, IT Equipment,
Operation (and Renewable Energy) -August 2012

Japan National Body
/Green IT Promotion Council

1.

Introduction

While Information and Communication Technologies (ICT) contribute greatly to
improvement of energy efficiency, energy consumption by ICT themselves has been
increasing. In particular, datacenters integrating large number of servers consume
enormous energy and their consumption level is elevating year by year. Therefore, urgent
action should be taken to control the CO2 emission indirectly introduced by the increasing
usage of Electricity by datacenters. This could be achieved by enhancing the energy
usage efficiency.
To enhance efficiency of datacenters, it is essential to evaluate it in a quantitative
manner as a first step. Currently, “Power Usage Effectiveness” (PUE) is a widely
acknowledged metric to evaluate energy efficiency of a datacenter. However, PUE is ametric to measure the energy efficiency of the datacenter facility. For improvement of theenergy efficiency of a datacenter, it is necessary to improve both efficiency of the facility
side and the efficiency of IT equipment.
To address this issue, “Green IT Promotion Council” (GIPC) has promoted thedevelopment of “Datacenter Performance per Energy” (DPPE), a set of novel metrics that
describe the entire energy efficiency of a datacenter (as KPIs) and a Holistic Framework
for evaluation of the datacenter efficiency by using these metrics. DPPE integrates anindex to express efficiency of IT equipment in addition to PUE, an index to express energy
efficiency of the facility. Besides, DPPE includes a metric evaluating the usage of Green
Energies such as Photovoltaic power generation and Wind power generation. Datacenter
operators and datacenter users could evaluate the efficiency of their entire datacenter(both Facility and IT equipment) objectively. This enables datacenter owners/operators toapproach the initiative on continuous basis by visualizing the process of improving thefacility and equipment as well as the efforts to rationalize their operations.
GIPC is currently promoting the realization of a set of metrics and the Holistic
Framework in harmonization with both Japan domestic and International groups
concerned including The Green Grid – TGG (USA) 1 . Two objectives as below are
established as GIPC’s goals in the realization:(1) Establishment of “Effective metrics”
An effective metric available for evaluation of the datacenter efficiency should beestablished.
(2) Proposal of a widely usable measurement method
A widely usable measurement method based on the facts of datacenters should bepresented. To achieve these goals, GIPC has tried to establish a method to measure theenergy efficiency by inclusive of existing systems as much as applicable and usable.

1“Harmonizing Global Metrics for Data Center Energy Efficiency 2011-02-28”(http://www.greenit-pc.jp/topics/release/pdf/dppe_j_20110228.pdf)

1Having above-mentioned situation as a background, GIPC is contributing thisdocument to fulfill the following objectives2:
Indicates basic definition of a set of metrics of DPPE and the Holistic Framework.
Explains how these metrics are linked with the improvement of energy efficiency of
datacenters (explained in Chapter 2)

The Demonstration of the experimental data gathering project conducted between
the years 2010 to 2011 with GIPC metrics developed for the energy efficiency has
yielded the current status of the Japan domestic datacenters. Furthermore, it has
indicated how to understand the energy characteristics of datacenters and promote
the energy saving approaches by the use of Holistic Framework (explained in
Chapter 3)
The specific details of the measurement method of each metric in datacenters aredescribed in “DPPE Measurement Guideline3” and this document will only outline thecharacteristic and points of each measurement method which will be explained in Chapter
2.

2

Definition of the metrics in this context is based on the proposal as of July 2012. The definitions andmeasurement guidelines may be susceptible to changes depending on the future discussion anddevelopments.
3“DPPE Measurement Guideline” Green IT Promotion Council (2011). See this Guideline for further
details of the DPPE measurement method.

2

2.

Basic definition of a series of metrics for energy efficiency
and Holistic Framework

2.1

Concept of energy efficiency improvement in a datacenter
According to the report published by the U.S. Environmental Protection Agency – EPA
in 20074, energy consumption of datacenters in the United States has more than doubled
from 28.2 billion kWh/year in 2000 to 61.4 billion kWh/year in 2006. To be more specific,
the energy consumption attributed to the facility side, such as air conditioning and power
distribution (datacenter infrastructure) and the energy consumption attributed to IT
equipment such as servers / storages / network equipments and etc. occupy about a halfof the total energy consumption of the datacenter respectively, and both of these parts are
ever increasing (Fig. 2-1). This result shows that it is essential to approach the
improvement of energy efficiency from both aspects of datacenter facilities and IT
equipment.

Fig. 2-1: Annual power consumption in US datacenters
The structure of energy consumption in a datacenter is shown in Fig. 2-2, from theprocurement of energies to the energy consumption by IT equipment. First of all, energies
for datacenters are procured as a commercially available power, Energy supplied from
off-site sources and also Energy supplied from on-site sources. Then, the procuredenergies are consumed by the datacenter facilities (infrastructure) such as air conditioners,
power distribution and lighting to provide a datacenter environment. Finally, energies wereconsumed by IT equipment.
Approaches to improve the datacenter energy efficiency could be implemented ineach of these parts. At first, with the part of the energy procurement, quality of the energies
4“Report to Congress on Server and Data Center Energy Efficiency Public Law 109-431” (USEnvironmental Protection Agency (EPA), 2007)3

to be procured can be improved by introduction of Green energies. (yield the reduction of
CO2 emission). Secondary, with a part of facility (infrastructure), efficiency can be
improved by introduction of highly efficient equipment and improvements on operations.
Furthermore, the IT equipment category could be divided into Hardware / Software andOperation parts. Efficiency of the IT side could be improved by introduction of highly
efficient IT equipment, and improvements of IT equipment Operations will complement
both.
Datacenter
Power
plant
Green

Grid power
Energysupply from
off-site

Normal

Green
Normal

Datacenter
environment

Air conditioning
Power supply

On-siteenergy supply

Energy procurement
Purchasing ofgreen energies

IT equipment

Hardware

Softwareandoperation

Lighting etc.

Operation of datacenter
infrastructure
Improvement of
datacenter infrastructure

Purchasing of IT
equipment

Operation of
IT equipment

Introduction ofhigh efficiency IT
equipment

Improvement of IT
equipment
operation

Fig. 2-2: Datacenter energy flow

By observing the division of energy flow of the entire datacenter, it is possible toexamine the area needed to improve for the efficiency possibility of the datacenter without
overseeing any specific components. Fig. 2-3 indicates an example for the process ofpromoting datacenter efficiency improvement performed by “Chief Information Officer”(CIO). Each part in Fig. 2-2 corresponds to the each operating unit (division) of any given
datacenter. In accordance of each line of the operating unit, (1) CIO could establish an
efficiency improvement goal for the entire datacenter first. Then, (2) the CIO could assign
each operating unit with an action plan as their goal to be achieved. (3) Each operating unit
(division), to which the goal is assigned, could promote their plans to achieve the goal in amanner most suitable to their responsibilities. Thus, by establishing linkages between theeach individual goal of operating units of the entire datacenter could yield the total
improvement of energy efficiency as a whole.

41. Operator establishes efficiency
goal of the entire datacenter

Entire Data Center Efficiency must
be doubled! What we need is….

CIO

We need to make
IT Equipment
utilization 20%
betterWe need to make
IT Equipment
efficiency 40%
betterCIO
We could
operate XX

We need to makeinfrastructure
efficiency 20%
betterCIO
We could
introduce
equipment with
20% better
efficiency

Operation Dept.

2. Communicates efficiency goalfor each metrics

We need toutilize green
energy 10%
more

CIO
We could
improve AC
efficiency by
xx %Purchasing Dept.

CIO
We could
use xx kW of
PV

Facility Dept.

3. On-site team
implements
plans toachieveefficiency goal

Facility Dept.

Fig. 2-3: Allocation of responsibility for each operating unit to improve energy
efficiency of the entire datacenter as a whole.

5

2.2

Metrics for Energy Efficiency of Datacenter Infrastructure, IT Equipment andOperation
Datacenter energy efficiency could be defined as a production per consumed energy.
Giving consideration to the energy procurement, Green energies could be deducted fromthe total energy consumption. Taking these factors into an account, energy efficiency =(data center production) / (non-green energy consumption).
As indicated in Chapter 2.1, improvement can be promoted efficiently by allocating theenergy efficiency of the entire datacenter to each part. In order to approach themeasurement task, the DPPE metrics were developed. DPPE has 4 specific metrics to be
used to evaluate efficiency of each part (Table 2-1). By categorizing each of the energy
efficiency, which is linked with the activities to improve the energy efficiency such as
selection of the IT equipment and facility improvements in each part of the datacenter isshown in Fig. 2-2 and Fig. 2-3.
Table 2-1: A set of metrics composing the energy efficiency of a datacenter (DPPE)
Sub-metrics
description

Equation

Corresponding action

ITEU(IT Equipment
Utilization)= Total energy consumptionof IT equipment (actual) /Total
rated energy consumption of
IT equipment (rated)

ITEE(IT Equipment
Energy Efficiency)
PUE(Power Usage
Effectiveness)= Total rated capacity of IT
equipment (rated) /Total rated
power of IT equipment (rated)= Total energy consumptionof datacenter (actual) /Totalenergy consumption of IT
equipment (actual)

GEC(Green Energy
Coefficient)= Energy level generated bygreen
energies(naturalenergies such as photovoltaic
power / wind power) (actual)/Total energy consumption of
datacenter (actual)

Efficient operation of IT
equipment: Approaches toimprove utilization ratio throughconsolidation and virtualization
and by reducing the number of
equipment in operation.
Approaches to introduce IT
equipment with higher energy
saving performance.
Approaches to reduce energy
consumption
causedbyfacilities such as efficiency
improvementforairconditioning system and powertransformer system as well as
the programs to use naturalenvironment effectively
Approaches to install and use
equipment to generate green
energies such as photovoltaic
power, wind power andhydraulic power etc.

These four specific metrics (ITEU, ITEE, PUE, GEC) reflect the four types of
independent approaches of energy savings. These were designed that any given one typeof energy saving approach should not affect other metrics. In accordance, each of theseelements could be used as a stand-alone metric also.

6

In this context, ITEU is the metric used for the efficient operation of datacenter IT
equipment. In a datacenter where no IT equipment is operating, ITEU value is “0”. On theother hand, in a datacenter where IT equipment are all fully in operation (100% utilized),
ITEU value is “1”.
ITEE is the metric to express the relationship between potential capacities of IT
equipment in a datacenter and the energy consumption. ITEE is an average efficiency of
all IT equipment installed in a datacenter. This value becomes larger when IT equipmentwith improved energy saving (efficient) is introduced (installed/replaced with).
PUE is the metric to express Datacenter’s facility energy efficiency. With increases inenergy consumptions in a datacenter facility, the PUE value becomes smaller and willapproach closer to “1”.
GEC is the metric for the amount of the usage of Green energy. It will become larger
when Green energy usage increases, such as introduction of Photovoltaic and Wind based
power and etc. (i.e. energies which would not generate CO2 in production).
These four metrics (where only with PUE, an inverse is used) were defined in the waythat the energy efficiency should improve when each metrics’ value would become larger.
By the usage of these four types of sub-metrics, it is possible to identify the efforts toimprove the energy efficiency of the entire datacenter (Fig. 2-4). The four sub-metrics
correspond to the energy saving approaches of each line units (division) indicated in Fig.
2-3.

To improve energy efficiency of the entire datacenter.
Goal

Operation of eco-friendly and sustainable datacenter

Reduces energy consumption of
IT equipment in datacenter

Reduces energy consumption ofsystems in datacenter

Measures

Uses introduced IT equipment
at optimal efficiency

Introduces IT equipment withgood energy efficiency

Reduces % of energy
consumption by equipment

Replaces commercial powerwith green energies

Metrics

ITEU
IT Equipment
Utilization

ITEE
IT Equipment
Energy Efficiency

PUE
Power Usage
Effectiveness

GEC
Green Energy
CoefficientFig. 2-4: Breakdown of overall datacenter energy efficiency: DPPE

7

2.3

Outline of IT Equipment Utilization (ITEU)

2.3.1 Definition of the metric
ITEU is a metric to indicate how efficient IT equipment is used in the performance. This
metric contributes to the enhancement of IT equipment utilization ratio and specific only tothe server, reduction of server idling power usage. It indicates the level of energy saving
achieved by virtualization technology and operational aspects of engineering that enableefficient use of potential capacity of IT equipment.
Because ITEU is the metric to indicate utilization ratio of the IT equipment, it is desirable
to obtain the ratio of actual performance (such as server utilization ratio) versus the rated
performance for each IT equipment type such as serer, storage, network and otherequipment and to calculate the average. However, it is practically difficult to measure theutilization ratio of all types of IT equipment in many datacenters at present time. Therefore,“the ratio of the total measured power level versus the total rated power level of IT
equipment” is used as a substitute for this metric’s concept. This method is used, because
the measured power level is considered to approach the rated power level with the increaseof equipment utilization ratio.
ITEU = Total energy consumption of IT equipment (actual) [kWh]/Total rated energy
consumption of IT equipment (rated) [kWh]The calculation scope for ITEU includes all the IT equipment including server, storage,
network and other IT equipment. All the IT equipment that are redundant in normal
operation status shall be included in calculating the ITEU. On the other hand, stand-by
state equipments in the power shutdown status shall be excluded from the calculation
because no power consumption occurring. The measurement period and measurement
points of the measured power level shall be based on the published measurement methodof “PUE measurement guideline”.
ITEU is the weighted average of the power utilization ratio where the rated power ofindividual IT equipment is regarded as weight:
ITEU = 1 / (total rated energy consumption)× (Σ (rated energy consumption of individual equipment) × (energy consumption ofindividual equipment)/(rated energy consumption of individual equipment))
Therefore, IT equipment with larger rated power will have bigger effect to the entire
ITEU number.
2.3.2 ITEU calculation example
If measured power consumption level of an IT equipment is 396,000 kWh during themeasurement period (30 days), and the total rated power level is 1,500 kW × 24 hours ×
30 days=1,080,000 kWh:ITEU = 396,000[kWh]/1,080,000[kWh] = 0.367 = 36.7 [%]

8

2.4

Outline of IT Equipment Energy Efficiency (ITEE)

2.4.1 Definition of the metric
ITEE is defined as total rated capacity of IT equipment divided by total rated power of
IT equipment (rated work units). Promotion of the energy saving is targeted by encouraged
to introduce an equipment with higher processing capacity per unit of power consumed.
ITEE = Total rated capacity of IT equipment [Work] / Total rated power of IT equipment [W]However,
Total rated capacity of IT equipment [Work] = α × Σ (Server capacity [GTOPS]) + β × Σ(Storage capacity [Gbyte]) + γ × Σ (Network capacity [Gbps])
In ITEE calculation, all of IT equipment composing a datacenter are classified into 3
categories (i.e. server equipment, storage equipment and network equipment) for
simplification. The equipment impossible to classify into any of these categories are
excluded from the scope of the total rated capacity calculation.
Because ITEE is a rated work rate, a simple calculation method is adopted for the
rated performance and the rated power, where IT equipment specification values forenergy saving performance cited (published) in catalogues are used. However, catalogue
values may not be used for some cases due to some cases such as “old equipment
purchased before the mandatory description of energy saving performance” or “out of thescope of mandatory description” and etc. Therefore, with regards to the total rated power
and the total rated capacity, the total value obtainable of the IT equipment shall be used forboth of these metrics calculations.
With regard to the server capacity, storage capacity and network capacity, two types ofapproaches have been discussed. The first approach is based on the adoption of theenergy consumption efficiency cited in the Energy Saving Act, as it has been consideredas the easiest value to use in the Japan domestic arena. The second approachemphasizes the convenience in the global arena based on the globally common elements.
Specific definition of each approach is described in Section 2.4.2 and Section 2.4.3.
α, β and γ are the coefficients to integrate the capacity of server, storage and network
equipment. α is defined as an inverse of the energy consumption efficiency of the standard
server as of 2005. Respectively, β and γ are the inverses of the energy consumption
efficiencies of the standard storage and network equipment as of 2005. Therefore, “Total
rated capacity of IT equipment” is the weighted average of the capacity, where the inverseof energy consumption efficiency for the standard server, storage and network equipments
as of 2005 is regarded as “weight”. If ITEE is calculated only for server equipments,
storage equipments or network equipments, the ITEE constitutes the ratio versus“processing capacity per unit energy” (total rated capacity / total rated power) of thestandard server, storage or network equipment as of 2005. Common value for α, β and γ
shall be used even if the datacenter in scope or period of calculation is different.
In the same way as with ITEU and ITEE are defined as the weighted average of theefficiency of IT equipment, where the rated power of the IT equipment serves as weight.
Therefore, the contribution to the ITEE value is larger if the IT equipment has a higher
rated power.

9

ITEE = α × Σ (Server capacity) +… / Total rated energy of IT equipment= Σ (Rated energy × α × Server capacity / Rated energy) + ... / Σ (Rated energy)= Σ (Rated energy × Normalized energy efficiency) / Σ (rated energy)
Based on the definition, the ITEE value of the datacenter that has average equipment
as of 2005 is set as “1”.
Furthermore, the product of the ITEE, which is the rated work unit, and the ITEU,
which is the actual utilization ratio, could be considered with an approximation of theenergy efficiency proxy of the IT equipment at a datacenter in operation.
2.4.2 Definition of capacity (Japan domestic application)
At present, there is no internationally unified method to compare capacity or energy
consumption efficiency of IT equipment (server, storage and network equipment) which areessential to calculate the total rated capacity of the IT equipment. Because description of
the value of energy consumption efficiency is mandatory in Japan domestic catalogues
based on the Energy Saving Act, this value is used as a server performance in ITEE
calculation, when application is limited to the Japan domestic market (Table 2-2).
Table 2-2: Equipment capacity used in the Energy Saving Act
Capacity

Remarks

ServerComposite theoretical performance-

Storage

Memory capacity-

Network equipment

Transmission capacity (Throughput)

Currently in review

W/MTOPS was used as the unit of the energy consumption efficiency of servers in2007 Version of the Energy Saving Act, while revision was made to use W/GTOPS from2010 Version. Therefore, a conversion using the following equation is necessary whenusing the values for the energy consumption efficiency appeared in 2007 Version.
Value equivalent to the energy consumption efficiency [W/GTOPS] in 2010 Version= Energy consumption efficiency [W/MTOPS] in 2007 Version × 1,000[Calculation of Coefficients α, β and γ]
Coefficients α, β and γ can be obtained in the following method:
Based on the research result of the Green IT Promotion Council5, typical capacity andpower consumption of server, storage and network equipments as of 2005 are determined
as indicated in Table 2-3.

5FY2009 Report by Research Analysis Committee, Green IT Promotion Council(http://www.greenit-pc.jp/activity/reporting/110628/index.html
10Table 2-3: Typical equipment as of 2005

ServerStorage
Network equipment

Capacity (a)Powerconsumption (b)36 GTOPS/unit
300 Gbyte/unit
4.2 Gbps/port

278 W/unit
28 W/unit
30 W/port

Processing capacity perunit energy (a / b)

0.129 GTOPS/W
10.7 Gbyte/W
0.14 Gbps/W

Values as below are obtained based on the definition that Coefficients α, β and γ are
the inverses of “processing capacity per unit energy” of the standard equipment:
Server equipment coefficient α = 1/0.129 = 7.72 [W/GTOPS]
Storage equipment coefficient β = 1/10.7 = 0.0933 [W/Gbyte]
Network equipment coefficient γ = 1/0.14 = 7.14 [W/Gbps]
2.4.3 Definition of capacity (Global application)
While the energy consumption efficiency is used as IT equipment capacity due to its
wide penetration in the Japan domestic IT Industry, it is necessary to use more globally
established metrics for IT equipment capacity calculation when calculating ITEE for Global
audience. At present, it is considered advantageous to use following metrics as the metrics
to satisfy the above-mentioned requirement:
Server capacity = Adjusted Peak Performance (APP)
Storage capacity = Capacity of Storage
Network equipment = Transmission speed of network equipment
In the same way as with the Coefficients α, β and γ in Japanese Version, these values
are obtained from estimated data of the typical equipment as of 2005 shown in Table 2-4.
Table 2-4: Typical equipment as of 2005
Capacity (a)Powerconsumption (b)Processing capacity perunit energy (a / b)Server

1.8×10-3 WT/ unit

278 W/unit

6.5×10-5 [WT/W]

Storage

300 Gbyte/unit

28 W/unit

10.7 [Gbyte/W]

Network equipment

4.2 Gbps/port

30 W/port

0.14 [Gbps/W]

Values listed below are obtained based on the definition that Coefficients α, β and γ
are the inverses of “processing capacity per unit energy” of the standard equipment:
Server equipment coefficient α = 1.54 × 105 [W/WT]
Storage equipment coefficient β = 0.0933 [W/Gbyte]
Network equipment coefficient γ = 7.14 [W/Gbps]
While “APP” is the most promising metric for the capacity in the global version
currently, it might be used also as a server performance, if use of the U.S. EPA’s Energy

11Star 2.0 is more prevalent in the future.

2.4.4 ITEE calculation example
The following paragraph describes the calculation example of ITEE based on theJapan domestic version indicated in Section 2.4.2.
Because this is not a capacity in itself, but energy consumption efficiency is stated in a
catalogs based on the Energy Saving Act, it is necessary to determine the capacity of theserver and the storage by using the rated power and the energy consumption efficiency.
Server:Storage:

420 [unit], Max. Power consumption 209 [W], Energy consumption
efficiency 0.0016 [d Segment] 209 [W] /0.0016 [W/MTOPS] × 1000 = 131[GTOPS] /unit
42 [unit], Max. power consumption 4620 [W], Energy consumption
efficiency 0.025 [AAA] 4,620 [W] /0.025 [W/Gbyte] = 184,800 [Gbyte] /unit

With regards to the capacity of the network equipment, the definition is currently underreview in the light of the Energy Saving Act. In accordance, capacity of the network
equipment is calculated based on the available transmission speed per port (NB: it is not acommunication line speed).
Network:

84[unit], Max. power consumption 145[W], Max. Transmission speed 1[Gbps], number of port per 1unit 24(Among these, setting of 10/100 [Mbps] is possible for all ports. Further,setting of 1 [Gbps] is possible for 14 ports among them.)
10 × 0.1 [Gbps] + 14 × 1 [Gbps] = 15 [Gbps] /unit

ITEE can be calculated by multiplying the above-mentioned IT equipment capacity byeach unit number.
ITEE = (7.72 [W/GTOPS] × 130 [GTOPS] × 420 [unit]+ 0.00933 [W/Gbyte] × 184,800 [Gbyte] × 42 [unit]+ 7.14 [W/Gbps] × 15 [Gbps] × 84 [unit])/ (209 [W] × 420 [unit] + 4,620 [W] × 42 [unit] + 145 [w] × 84 [unit])= 3.9812

2.5

Outline of Power Usage Effectiveness (PUE)

2.5.1
Definition of the metric“PUE” is the metric to measure the power efficiency of the datacenter which was atfirst proposed by The Green Grid (TGG), in 2007. It allowed an easy measurement of theenergy efficiency of the datacenter.
In this context, the definition of PUE is based on the TGG’s White paper and theagreement at the International Conference in February 2011 (Global Harmonization of
Datacenter Energy Efficiency Metrics)6. Additionally, the details of the measurement were
determined by the Japan Data Center Council (JDCC) as a standard.
PUE is obtained by dividing the total energy consumption of a datacenter (entire
energy consumption of the datacenter as a whole facility) by energy consumption of theentire IT equipment (Fig. 2-5). It is a metric to indicate/gauge how many time of energy in aratio at a datacenter (entire datacenter as a whole facility) in energy consumption for its
operation when compared to the energy consumed by the entire IT equipment.
PUE = Total energy consumption of datacenter [kWh]/Total energy consumption of IT
equipment [kWh]Smaller PUE value represents better energy efficiency. This value must be higher than“1.0”. A datacenter has better energy efficiency when PUE approaches “1.0”.
Facility dedicated to datacenter
Energy sources ofdatacenter
1) Commercial power
2) Photovoltaic / Windpower generation
3) Power from emergency
generators
4) In-house power
generation facility5) Heat source including
boilers6) DHC etc.

Total energy
consumption of IT
equipment

IT equipment

Air conditioning
system Monitor center system Electric system /Building management
system Security system Datacenter operation
 Office equipment
 Elevator Lighting (common floor) Others

Total energy
consumption of
datacenter

Total energy
consumption of
datacenter

PUE =Total energy
consumption of IT
equipment

Fig. 2-5: PUE concept for dedicated type datacenter

6“Harmonizing Global Metrics for Data Center Energy Efficiency 2011-02-28”(http://www.greenit-pc.jp/topics/release/pdf/dppe_j_20110228.pdf)

13

2.5.2Type of datacenter energy source and measurement points of total energy
consumptions
The total energy consumption of an entire datacenter facility includes all the energy
consumption with the use of air conditioning, lighting and other infrastructures besides theenergy consumption by IT equipment.
The total energy consumption of the entire datacenter facility is measured at theinterface between the entire datacenter facility and the external area (Utility handoff). The
measurement shall include all the energy sources (commercial power, fuel oil, gas andetc.) (Fig. 2-6). The Energy in the scope of measurement shall be measured as Source
Energy Level (to be described later in detail: Initial energy level that generates energy:
petrol level, gas level etc.) and all energies are summed up and converted into theelectrical power equivalent (kWh).

Measurement
interface

Photovoltaic power
generation
Wind Windpowerpower
generation
generation

Entire datacenter facility

Commercial
powerPower receiving
systemFuel system(sourceconversion)

In-house power
generation

Fuel system (source conversion)/ Secondary energies (chilledwater etc.：source energy
conversion)

IT equipment

Air conditioning system

Fig. 2-6: Interface between entire datacenter facility and external area: Utility
handoff
The main energy sources to be used by the datacenter currently include; (1) power
supplied by the electric utility, (2) photovoltaic power and wind power generations, (3)power from emergency generators and (4) power from in-house generator facility such
would include “co-generation”. Besides electric power, a datacenter may use non-electricenergies for thermal source of air conditioning such as (5) boilers using gas and heavy oil
for fuels as well as heat supply from in-house co-generation facility, and (6) heat supply
from District Heating & Cooling (DHC) service (Fig. 2-7).

14

Datacenter

Electric power
Calorific value
Measurement point
for total energy
consumption of
datacenter
1) Commercial
power of electricutilities2) Photovoltaic
power generation
Wind power
generation

Power receiving
system3) Emergency
generator

UPS

PDU

IT equipment(B)4) Essential in-house
power generation(co-generation)

Fuel tankServer room
Measurement point for totalenergy consumption of IT
equipment

Estimation from (A) and (B)Air conditioning
system(A)
Fuel tank5) Heat source
including boilers
etc.
Fuel tank6) Heat sourceof DHC

Fig. 2-7: Type of datacenter energy sources
The summed total of all of these energies are needed for the PUE calculation.
Because it is necessary to sum up the electric energy and other types of energies upon
unifying their units, [kWh] is used as the basic unit for all energies. As indicated in Chapter
3, PUE will show monthly fluctuation depending on the seasons. Therefore, it isrecommended to use annual average in its reporting. When reporting PUE values
measured in different time period of the year, clear indication of the measurement period(month/season) is recommended.
When converting the energy from non-electric energy sources into [kWh], the calorificvalue based on energy type converted into kWh cited in “The Ordinance for Enforcementof the Energy Saving Act” is used.
The measurement points for the total energy consumption of datacenter shall be thepower level at the specific power feed input point of the datacenter (i.e. inlets of powerreceiving unit, outlets of photovoltaic and wind power generation units, and outlets ofemergency generators and essential site power generation [co-generation] units)
respectively. The loss associated with power generation and power transmission shall be
excluded from the calculation.
Energies from non-electric energies such as those from boilers shall be calculated
from their fuel consumption and etc.
In Section 2.5.3, a method of the PUE calculation in the presence of a heterogeneousenergy sources is explained. In Sections 2.5.4 - 2.5.5, outline for the PUE measurement
points are explained.

2.5.3

Types of energies in the scope of consumption and the concept of energy
totaling
The types of energies in the scope of consumption include all types of energies
15

including electric power, fuels (heavy fuel oil, gas etc.) and secondary energies (chilledwater for DHC service and etc.). Particularly, attention should be given to the fact thatheavy fuel oil and gas and etc. are used for the cooling equipment or power generation
unit.
Concept of source energy level
The basic concept is that all types of energies should be converted into electric power
level before summing/totaling.
Considering as the energy level for creating 1 kWh commercial power (a), an energy
of approximately three times (source conversion coefficient δ) is needed (δ×a), whengiving a consideration to the power generation efficiency at the power plant and the powertransmission loss from the power plant to the datacenter. This energy level ofapproximation of three times is called “source energy level.” Contrary to the fact, theenergy level (a) [kWh] that does not consider these losses is called “site energy level.”
An agreement was achieved in the International Conference in February 2011 that thebasic approach is to use this source energy level for evaluating the energy level of adatacenter.
Meanwhile, with regards to the fuel energies, the energy consumed in the datacenteris regarded as the source energy level. The amount of heavy fuel oil and etc. converted
into electric power level (b) [kWh] is used.
With regards to the secondary energy (c) such as the chilled water for DHC service,
the amount of energy needed to make the chilled water (source conversion coefficient ε) isused upon conversion into “electric power level (ε× c)” [kWh].
Totaling method based on the source energy level
Based on the above-mentioned concept, the summing/totaling of the source energies
could be expressed with the following calculation equation:
Total source energy = δ× (a) + (b) + ε × (c)However, if this equation is used for summing/totaling, a significantly high PUE may
occur for datacenters where majority of the energy is supplied from Commercial electric
power. Because portion of the energy attributed by the use of the commercial power isexpressed with the value three times higher than other energies, the apparent electric
power level in these facilities could become three times higher than other facilities,
resulting in higher PUE value. This is compared with the conventional PUE announced by
TGG.
In accordance, to consolidate the scale with conventional PUE value, the entire levelhas been normalized by using the value of the conventional commercial electric power(power meter value), so that the entire level should be equivalent to the site energy level.
DC total energy consumption = (a) + (1/δ) x (b) + (ε/δ) x (c)Following values are used globally for source energy conversion coefficients:*Source energy conversion coefficient for commercial electric power:* Source energy conversion coefficient for fuels:
1/δ* Source energy conversion coefficient for secondary energies: ε/δ

16=> 1.0=> 0.35=> 0.40

Table 2-5: Source energy conversion coefficient for each energy
Energy type
Commercial electric powerGas (natural gas, city gas etc.)
Heavy / Light fuel oil etc.
Other fuels
Chilled water etc. (DHC)

Source energy conversion coefficients
1.0
0.35
0.35
0.35
0.40

In accordance with the consideration of the source conversion coefficient in the totalenergy consumption level, an average source energy conversion coefficient of electric
power consumed in the datacenter is given a consideration with regards to the energy
consumption of IT equipment.
Total energy consumption of IT equipment= Site energy of IT equipment × Average source energy conversion coefficientsfor DC power,
where
Average source energy conversion coefficients for DC power= (Source energy used for procurement / generation of total electric power
consumed in the DC)/ (Site energy of total electric power consumed in the DC).
Based on above, following equation is obtained:
PUE =DC total energy consumption/ Total energy consumption of IT equipment7

2.5.4
Measurement points for the total energy consumption of IT equipment
The point for measuring energy consumption of IT equipment (electric power level)
should be the output of the PDU (power distribution unit). If the measurement is conductedat the UPS output, the power loss between the UPS and IT equipment should beconsidered. (Fig. 2-8). If there are multiple “Down Transformer” between IT equipment andthe measurement points, the loss associated with the Down Transformer and cablings andetc. should be given a consideration. If the measurement of the PDU output is infeasible,
the measurement results collected in the substitute measurement points shown in thefollowing figure should be used. These results are converted into the values equivalent to
the PDU output measurement based on the method specified in the guideline.

7

Harmonizing Global Metrics for Data Center Energy Efficiency 2011-02-28” Some examples of PUE
calculation is shown in (http://www.greenit-pc.jp/topics/release/pdf/dppe_j_20110228.pdf).

17

Commercial
power by electricutilities

Power receiving
systemHigh voltage
receiving panel

High voltage distribution panel
Substitutemeasurement
point (No.2)
UPS loss

Transformer /Cable loss

UPS

UPS
Substitutemeasurement
point (No.1)

PDU

Air conditioning
systemPDU
Measurement point for energy
consumption of IT equipment(PDU output total)

IT equipment

IT equipment

Rack

Rack

 Monitor center system Electric system /Building management
system Security system Datacenter operation
 Office equipment
 Elevator Lighting
 Others

Server room

Fig. 2-8: Measurement points of IT equipment*Measurement of power factor
The actual total power consumption (kW) of IT equipment is calculated by - Voltage(V) × Current (A) × Power factor (%), which is different from kVA = Voltage (V) × Current(A). Therefore, in calculating the PUE, it is recommended to give consideration to thepower factor.
If the total energy consumption of IT equipment (power level [kVAh]) is obtained
without giving any consideration to the power factor, the value of the total energy
consumption of IT equipment becomes larger than the actual level. This may lead tosignificantly more advantageous data for the PUE calculation.
Therefore, when measuring the total energy consumption of IT equipment (power
level [kVAh]), measurement should be conducted by giving a consideration to the power
factor instead of just calculating the value by the use of - Current [A] × Voltage [V]equation.
While power factor depends on the system configuration of the IT equipment
downstream of the PDU, it is known from the actual sample data obtained that thechronological fluctuation maybe small. Therefore, it is acceptable to obtain the total energy
consumption of IT equipment by multiplying the power level measured continuously incurrent / voltage. An average value of the power factor measured by using portableterminals and etc. on temporal basis in the past could be utilized. If the power factor is
unknown or impossible to measure, then the “95%” may be used as a recommended18

substitution value.
Table 2-6: Example of the power factors determined on actual data samplings
Ave

Var

Max

Min

95.8%

46.6%

100%

64%

2.5.5

How to obtain the datacenter total energy consumption for a non-Standalone
type datacenter
Many datacenters are accommodated in a combined mixed building facility, where
both the datacenter function (floor) and non-datacenter function (floor) such as general
office irrelevant to the datacenter would co-exist within one site. When calculating the PUE
in such, only the total energy consumption used for the datacenter function should be
extracted from the total energy consumption of the entire building (which includes theenergy consumption associated with non-datacenter function).
For the mixed facility non-Standalone type datacenters, the usage function of thebuilding floor could be divided into three types (datacenter function, non-datacenter
function and common function.) In this context, the common function refers to theequipment / floor in the building which is commonly used to manage, maintain and operate
both datacenter and non-datacenter functions. To be specific, following equipment areincluded; (1) Management system and security system for the entire building. (2) Air
conditioning, Elevators and Lightings of the common floors. Besides these, Hall area,
Corridors, Bathrooms and shared use Meeting rooms in the building would be included
into this category.
The total energy consumption used by the datacenter function could be defined as thesum of the energy spent for the datacenter function and an adjusted part of the energy
used by the common function. With regards to the energy spent for the common function,
an appropriate ratio should be determined between the part of the consumption by the
datacenter function and the part of the consumption by the non-datacenter function(general office and etc.). With all these, only the part of consumption for the datacenter
function should be added to the already measured energy consumption for the dedicateddatacenter function.
To be more specific, the calculation is carried out in the following process;First, the energy consumption for the datacenter function and the energy consumption
for the non-datacenter function are deducted from the energy consumption of the entire
building. And then the energy consumption for the common section is calculated. Theenergy consumption for the common section is divided into the energy consumption
associated with Air conditioning and the energy consumption associated with the otherconsumption sources (Fig. 2-9). The energy consumption associated with Air conditioning
in the common function area is sub-divided into the ratio between the power levels for theIT equipment + related systems in the datacenter section and the power level of thenon-datacenter function (i.e. OA power for office section and etc.). Additionally, the energy
consumption other than those from Air conditioning such as the consumption associatedwith Lighting and Elevators and etc. are divided into both for datacenter function andnon-datacenter function parts (office part and etc. - excluding the area for shared use, such

19

as Hall area, Corridors, Bathrooms and Meeting rooms) based on the area of percentage.
For the simplification purpose, it is acceptable to include all of the energies spent forthe common function into the energy consumption of the datacenter function in calculation.
In this case, however, the PUE could worse than the actual usage situation.
Furthermore, if the PUE is calculated solely for the purpose of the volunteered
approach for datacenter improvements, an approach to emphasize ease of measurement
may be acceptable by neglecting the energy consumption in the common area regardlessof the above-stated methodology 8 . However, it should be noted that the PUE value
differences from those considering the impact of calculating common area could beobtained in this case.
Composite facility
Total energy
consumption by IT
equipment

Datacenter function
IT equipment

Air conditioning system
dedicated to datacenter

Consumption by
dedicated datacenter
function Monitor center system Datacenter operation
Office equipment
 Others

Common functions
Common powerreceiving system / airconditioning heatsource for building

Non-datacenter function

Total energy
consumption by
datacenter function

Common air
conditioning system

Adjustment based
on % of power
consumption

Consumption by
datacenter function

 Electric system /Building management
system Security system Elevator Lighting (common
floor) Others

Adjustment based
on % of dedicatedfloor area

Consumption by
non-datacenter
function

Power source such as
receptacles in general
office section not related
to datacenter function

Air conditioning system
dedicated to office

Fig. 2-9: Schematic image of a combined mixed-use type datacenter(non-Standalone), and how to determine the total energy consumption

8“Harmonizing Global Metrics for Data Center Energy Efficiency 2011-02-28”(http://www.greenit-pc.jp/topics/release/pdf/dppe_j_20110228.pdf)

20

2.5.6
PUE calculation example
It is assumed that the datacenter receives commercial power of 350,000 [kWh], power
supply of 40,000 [kWh] from the photovoltaic power generation and consumes 20.3[kl](kilolitre) boiler fuel (A heavy fuel oil) for Air conditioning. The power consumption by IT
equipment is assumed as 30,000 [kWh] (However, all boilers are used to generate chilledwater).
In this case.
PUE = (350,000[kWh] + 40,000[kWh] + 20.3[kl] ×10,861[kWh/kl]) / 30,000[kWh]= 2.03(Average source energy conversion coefficients for IT power = 1.0. )

21

2.6

Outline of GEC (Green Energy Coefficient)

2.6.1
Definition of the metric
GEC is the value obtained by dividing the Green energy, or Renewable energy,(Photovoltaic power, Wind power, and etc.) used in a datacenter within the total energy
consumed by the datacenter.
GEC = Green energy [kWh] used in DC site/Total energy consumption of datacenter[kWh]
The Green energies in this context refer to the energies generated from Naturalenergies such as Photovoltaic power and Wind power. Among these, the scope of the
GEC includes the part of Green energies (electric power and etc.) legally entitled to thedatacenter. Namely, following rules were applied:The precise definition (scope) of Green energies should be the scope adopted by
Tradable Renewable Energy Certificates and etc. of each country.

If Tradable Renewable Energy Certificates are purchased, the purchased amount isalso included in the Green energies.

The energies generated on the datacenter site whose title is not sold to external
parties as Tradable Renewable Energy Certificates are also regarded as Greenenergies. Application of the Tradable Renewable Energy Certificates system is notalways necessary, as long as the energy is generated and consumed within the
Datacenter boundary/site. The measurement points for on-site power generation of
Photovoltaic power and Wind power and etc. are indicated in Fig. 2-10.
To maintain consistency with the total energy consumption, [kWh] is used for theenergy basic unit.
The energies using the exhaust heat from any system used for other purposes (e.g.
boilers for product manufacturing and etc.) are not included in the Green energies, even if
they are generated on site, because these are regarded as equivalent to the thermal
source for boilers or DHC service and etc.

22Datacenter

Electric power
Calorific value
Measurement point
for total energy
consumption of
datacenter
1) Commercial
power of electricutilities
Off-site
photovoltaic
power generation,wind power
generation,
hydraulic power
generation andbiomass power
generation etc.

2) Photovoltaic
power generation
Wind power
generation

Power receiving
system3) Emergency
generator* GEO
measurement
point
UPS

PDU

IT equipment(B)4) Essential in-house
power generation(co-generation)

Fuel tankServer room
Measurement point for totalenergy consumption of IT
equipment

Measurement of energies
other than electric power
Air conditioning
system(A)
Fuel tank* Calculation as
equivalent tocommercial
power source5) Heat source
including boilers
etc.
Fuel tank6) Heat sourceof DHC
Reuse ofexhaust heatfrom plant etc.

* Calculation as equivalent to
DHC

Fig. 2-10: GEC measurement points and handling of the use of Green electric power /exhaust heat

2.6.2
GEC calculation example
It is assumed that the datacenter receives commercial power of 350,000 [kWh], power
supply of 40,000 [kWh] from Photovoltaic power generation and consumes 20.0[kl](kilolitre) boiler fuel (A heavy fuel oil) for air conditioning.
GEC = 40,000[kWh] / (350,000[kWh] + 40,000[kWh] + 20.3[kl] × 10,861[kWh/kl])= 6.6 [%]

23

2.7

Rationality of selecting Four metrics
In Chapter 2.2, four types of metrics (ITEU, ITEE, PUE and GEC) were introduced as metrics
to compose DPPE, energy efficiency of the entire datacenter, by dividing energy consumption flowof the datacenter in a quantitative manner.
In a quantitative approach, the energy efficiency of the entire datacenter could be expressed
as below:
Energy efficiency= (Data center production) / (Non-green energy consumption)= (Data center production) / (Energy consumption of IT equipment)× (Energy consumption of IT equipment)/ (Energy consumption of datacenter)× (Energy consumption of datacenter)/ (Non-green energy consumption)= (IT productivity) × (Facility efficiency) × (Deduction of green energies),where
IT productivity: Production (Information processing by IT equipment) /Energy
consumption of IT equipment
Facility efficiency: Energy consumption of IT equipment/DC energy consumption (=1/PUE)
Deduction of green energies: DC energy consumption/non-green energy consumption(=1/ (1-GEC))
Further, if equations indicated below are valid in approximate manner:
IT productivity= (Average utilization ratio of IT equipment)× (IT equipment rated capacity) / (IT equipment rated energy consumption)
Average utilization ratio of IT equipment≒ (Actual consumption energy of IT equipment) /(IT equipment rated energy consumption),
It constitutes the following relationships:
Energy efficiency= (Actual consumption energy of IT equipment) / (IT equipment rated energy consumption)× (IT equipment rated capacity) / (IT equipment rated energy consumption)× (Energy consumption of IT equipment) / (Energy consumption of datacenter)× (Energy consumption of datacenter) / (Non-green energy consumption)= ITEU × ITEE × (1/PUE) × (1/ (1 - GEC))ITEU×ITEE was evaluated in the actual datacenter system as a part of experiment for IT
productivity proxy evaluation carried out by The Green Grid. As a result, it was demonstrated that
ITEU×ITEE was useful as a proxy of IT productivity and proved its simplicity in measurement.
Particularly, the ease of measurement is considered to be a significant feature of the ITEU×ITEE,
as emphasis is placed on the feasibility of actual measurement in any datacenter.
These results showed that the energy efficiency of the entire datacenter could be evaluatedby using these four metrics “ITEU”, “ITEE”, “PUE” and “GEC”. In an application of these metrics,
metrics are separated and differentiated from each other in each term of multiplications. There isno mutual influence to each other (mutually independent).
In accordance, these four metrics “ITEU”, “ITEE”, “PUE” and “GEC” are proved to be a24

combination which could evaluate the energy efficiency of the entire datacenter without any
redundant measurement efforts.
Based on the above, the GIPC proposes to construct a comprehensive evaluation system/comprehensive evaluation metrics by the use of these specific metrics comprised of “ITEU”,“ITEE”, “PUE” and “GEC”. This comprehensive evaluation system is called “Datacenter
Performance per Energy” - (DPPE) which was created by the GIPC.

25

2.8

Comprehensive energy efficiency evaluation using Four metrics - Holistic Framework As one of the approaches to evaluate the energy efficiency of the datacenter using metrics, amethod to indicate four metrics on a spider web chart is proposed. This method allows
understanding of the status of energy saving activities in a datacenter for each selected target andits approach. The comprehensive understanding of multiple aspects of the energy efficiency bythis approach is defined here as “Holistic Framework.”
As the combination of the four metrics is designed to allow evaluation of energy efficiency ofan entire datacenter, each metric could be allocated to each axis of the spider web chart. The eachcharacteristic indicated by each of the four metrics are shown as below:IT equipment operation efficiencyIT equipment hardware efficiency
Infrastructure efficiency

Utilization level of green power
Fig. 2-11 indicates an example of the spider web chart.
ITEU

ITEE

GEC

PUE

Fig. 2-11: Spider web chart of the Holistic Framework

In the spider web chart in Fig. 2-11, selected axes values for an individual datacenter could becompared with those numbers of average datacenters. In this way, datacenter operators could
understand the characteristics of each datacenter, and focus on their efforts with the largest aspect
for the improvement. For an example, if the hardware efficiency of the IT equipment is significantly
lower than that of the average datacenter, then a renewal of the IT equipment could be a promising
option for the improvement of the energy efficiency. Following these options could be considered
as countermeasures for the efficiency improvement;
Improvement of the IT equipment operationUse of the highly efficient IT equipment
Improvement of the infrastructure efficiency (Air conditioning, Power supply and etc.)Introduction of Renewable energies

26

3.

Introduction of the actual experimental DPPE measurment
projects - (Japan domestic and International collabolation)

With the purpose to verify the usability and the correctiveness of the energy saving metrics fordatacenters, and to understand the current status of energy consumption in datacenters, anexperimental project for energy efficiency measurements in datacenters were performed under the
coopration of 15 Japanese datacenter operators and 2 overseas datacenter operators (inSingapore and Vietnam). The experimental measurement period was from August 2011 toJanuary in 2012 (6 months) for domestic operators and a short duration in September 2011 for 2
overseas operators.
The outline and the outcome of the experimental measurement project are summarized in thefollowing secitons.

3.1

Implementation outline of the measurement projectWith the particiaption of 20 datacenters owned or operated by 15 Japanese datacenter
operators, measurements were carried out during 6 months from August 2011 to January 2012.
Data collected (i.e. Data Center and Eequipment power consumption and etc.), which werenecessary to determine the four metrics “PUE”, “GEC”, “ITEU” and “ITEE”. These measurements
were implemented in compliance with the “DPPE Measurement Guideline” created in previous
year by the GIPC work group. Prior to the experiment, the instruction and explanation meetings
were held for the participating datacenter operators.
The Implemetation Overview of the measurement project is shown in Table 3-1. The type offeasible data measured were different depending on the datacenter and not all could measure thedata on monthly basis due to some operational restrictions. The sample number of data collectedfrom datacenters were different depending on the metrics or time period (month).

27

Table 3-1: Outline of the measurement project outcome○Number of participant operators: 15 Japan domestic and 2 overseas operators○Number of participant datacenters: 20 Japan domestic and 2 overseas datacenters○Measurement period: August 2011 - January 2012
○Outline of participant datacenters Number of datacenters in each regional area:<Japan>Hokkaido / Tohoku / Hokuriku (3), Kanto (15),Tokai / Chubu / Kinki (0), Chugoku / Shikoku / Kyushu / Okinawa (2),<Overseas> (2) Singapore and Vietnum
 Datacenter establishment year range: 1985 - 2010
 Total floor area: Approx. 800 - 145,000 m2
 Floor area for server rooms: Approx. 10 - 16,500 m2○Number of data collected (based on each month) between 2011-2012
August September October November December January
PUE

19

19 (2)

18

18

17

16

GEC
ITEU
ITEE

19
16
11

19 (2)
16 (2)
11 (2)

18
15
10

19
16
11

18
15
11

16
16
11

The numbers in parenthese are those from overseas datacenters

28

3.2

Experimental Measurment results

3.2.1
PUE
Actual PUE in this project measured on monthly basis during the period between
August 2011 and January 2012 was; 1.70 - 1.85 on average (Fig. 3-1).
The maximum value in this period was; 2.34 and the minimum value was; 1.41.
These measurements were considered to be appropriately conducted.
The overseas measurement results for PUE were; 2.30 for Singapore and 2.02 in
Vietnam.

Aug.
８月

9

９月
Sept.

１０月
Oct.

１１月
Nov.

１２月
Dec.

１月
Jan.

8

データセンター数
Number of DCs

7
6
5
4
3

 2.02 (Vietnam)
 2.30 (Singapore)

2
1

0

- 1.2

Average
Minimum
Maximum

1.2 - 1.4

1.4 - 1.6

1.6 - 1.8
1.8 - 2.0PUE

2.0 - 2.2

2.2 - 2.4

2.4 -August(n=19)

September(n=19)

October(n=18)

November(n=18)

December(n=17)

January(n=16)

1.85
1.43
2.34

1.79
1.41
2.15

1.75
1.45
2.28

1.71
1.44
2.09

1.70
1.42
2.26

1.70
1.42
2.05

Fig. 3-1: PUE monthly distribution
Fluctuation of the average PUE based on each month revealed that the PUE in
August tends to be the highest due to its Hot Seasonal Climate with a high averagetemperature. Similarities were seen in the results from previous year. The PUE tends toshow lower value after September as the hot season ended (Fig. 3-2).

29

PUE Max.
/ Min. / Average
PUE
最大・最小・平均値

最大値
Max.

2.60

Min.
最小値

2.40

Average
平均値

2.20

PUE

2.00
1.80

1.60
1.40
1.20
1.00
８月
Aug.

９月
Sept.

１０月
Oct.

１１月
Nov.

１２月
Dec.

１月
Jan.

Fig. 3-2: PUE monthly average / Max. / Min. values
With regards to the relationship between datacenter age (number of years since built)
and the PUE, the PUE of the datacenters built within the last five years indicated relatively
lower PUE numbers. The PUE tends to became higher with older datacenters with theirage. However, the PUE of the datacenters built more than 20 years ago indicated lower
PUEs. This result was attributed by the fact much older datacenters did replacement theirolder air conditioning system to newer ones. This result demonstrates the improved energy
efficiency through the countermeasure (Fig. 3-3).
Years elapsed since DC establishment –PUE (August through January [next year])

2.60

PUE (Aug.)

PUE（８月）PUE (Sept.)

PUE（９月）

2.40

PUE
PUE(Oct.)
（10月）

2.20

PUE
PUE(Nov.)（11月）
PUE
PUE(Dec.)
（12月）

2.00

PUE (Jan.)

PUE（1月）

PUE

1.80
1.60
1.40
1.20
1.00
0

5

10

15

20

Years elapsed since DC establishment

Fig. 3-3: Datacenter age and PUE

30

25

30

Furthermore, the relationship of the PUE and “Total floor size of the building” vs.
“server room floor size” was analyzed. The datacenters with higher PUE tend to haverelatively smaller floor size. The result indicated that datacenters with larger than somecertain floor size could achieve better energy efficiency (Fig. 3-4).
Total floor area – PUE(August through January [next year])
延べ床面積－PUE（8～翌1月）

2.60

PUE (Aug.)
PUE（８月）PUE(Sept.)
PUE（９月）PUE(Oct.)
PUE（10月）

2.40

PUE(Nov.)
PUE（11月）

PUE

2.20

PUE
(Dec.)
PUE（12月）
PUE(Jan.)
PUE（1月）

2.00
1.80
1.60
1.40
1.20

0

5,000

10,00015,00020,00025,0002

Total
floor area (m )延べ床面積（㎡）

Fig. 3-4: Total floor size of datacenter building and the PUE

31

30,000

○Comparison with the results from previous year’s measurment.
The PUE measurement results were comprared with the PUE data collected in 3
months in the previous year (pre trial data collection). The comparison result demonstrated
that, in general, the PUE distribution observed in 2011 tends to show lower value than2010 (Table 3-2, Fig. 3-5).
Table 3-2: Comparison of average / Min. / Max. PUE between years
2010

2011

1.93

1.84

Average September

1.84

1.78

October

1.82

1.75

August1.22

1.43

Minimum September

1.18

1.41

October

1.76

1.45

August2.69

2.34

Maximum September

2.22

2.15

October

1.97

2.28

AugustPUE（9月）PUE(September)
datacenters (period total)
Number of
データセンター数(（期間合計）

datacenters (period total)
Number of
データセンター数(（期間合計）

PUE（8月）PUE(August)8
7

2010年
FY20106

FY2011
2011年
5

4
3
2
1

8

7

2010年
FY20106

FY2011
2011年
5
4
3
2
1
0

0

- 1.2

1.2 - 1.4

1.4 - 1.6

1.6 - 1.8

1.8 - 2.0

2.0 - 2.2

2.2 - 2.4

- 1.2

2.4 -

1.2 - 1.4

1.4 - 1.6

1.6 - 1.8

PUE（10月）
PUE(October)datacenters (period total)
Number ofデータセンター数(（期間合計）

7

6

2010年
FY20105FY2011
2011年

4

3

2

1

0

- 1.2

1.2 - 1.4

1.4 - 1.6

1.6 - 1.8

1.8 - 2.0

1.8 - 2.0ＰＵＥ

ＰＵＥ

2.0 - 2.2

2.2 - 2.4

2.4 -

ＰＵＥ

Fig. 3-5: Two years comparison of the PUE

32

2.0 - 2.2

2.2 - 2.4

2.4 -

To verify these trends, comparative analysis of the temperature and the PUE between
two years of measurments were conducted with those datacenters with available
data.(Table 3-3 shows the temperature and the PUE.)

Table 3-3: Two years comparison of the PUE based on datacenters(the numbers in parenthese in lower line indicate an average temperature of the same month)
Datacenter Description

August2011

1.81(27.1)

1.79(24.9)

1.72(19.2)

1.71(14.8)

1.63
(7.5)

1.60
(4.8)

1.71

2010

1.93
(28.6)

1.89(24.7)

1.81(18.6)

1.73(13.4)

1.70
(9.9)

1.64
(5.3)

1.78

2011

1.78
(27.5)

1.77(25.1)

1.70(19.5)

1.70(14.9)

1.69
(7.5)

1.67
(4.8)

1.72

2010

1.89
(29.6)

1.84(25.1)

1.77(18.9)

1.69(13.5)

1.76
(9.9)

1.77
(5.1)

1.79

2011

1.94
(27.1)

1.91(24.9)

1.87(19.2)

1.85(14.8)

1.84
(7.5)

1.84
(4.8)

1.88

2010

2.06
(28.6)

2.01(24.7)

1.97(18.6)

1.90(13.4)

1.89
(9.9)

1.88
(5.3)

1.95

2011

2.15
(26.7)

1.95(23.0)

1.87(15.4)

1.83*(10.9)

1.85*
(3.2)

-

1.99

2010

1.97
(26.2)

2.00(28.8)

1.82(16.7)

-

-

-

1.93

Datacenter A(Outsourcing etc.)

Datacenter B(Dedicated DC)

Datacenter C(Outsourcing etc.)

Datacenter D(SIer)

September October November December January

Average

Note: *This value is excluded from calculated average because the data for FY2010 is not available.

These data were plotted on the X-Y axis representing the temperature and the PUE,
and linear approximations for each distribution were conducted. As the result, gradient of
the approximation line in year 2011 is smaller than that of the 2010. It is indicating that theelevation of the PUE accompanying the temperature elevation is smaller in 2011 (Fig. 3-6).
This result suggests that sophistication of energy management has progressed, which
may be the result of attributable influences of Japanese nationwide power saving
campaign of the year 2011.

33

2.2

2011
2.1

2010
Linear(2011)線形 (2011)

線形 (2010)
Linear(2010)

2.0

PUE

1.9

1.8

1.7

PUE elevation withtemperature is slower
1.6
0

5

10

15

20
気温
Temperature

25

30

35

Fig. 3-6: Two years comparison of the PUE

3.2.2
GEC
The GEC indicates the percentage of Renewable (Green) energies which weregenerated and consumed within the datacenter site compared with the total energy
consumed by the datacenter.
With in the current measurements, 4 out of 5 datacenters generate power by
Photovoltaic system. The type of Natural energy, monthly total energy consumptions andmonthly GECs in each datacenter are shown in Table 3-4.
Table 3-4: Datacenters with GEC results
Datacenter A

Datacenter B

Datacenter C

Datacenter D

Datacenter E

Purpose /Application ofbuilding

Dedicated todatacenter

Dedicated todatacenter

Dedicated todatacenter

Dedicated todatacenter

Dedicated todatacenter

Type of naturalenergies

Photovoltaic

Photovoltaic

Photovoltaic(Unknown)

Photovoltaic

GEC (Average)

0.003360.000131

0.002057

0.003047

0.001937(note: Green power generation method of 1 datacenter is not specified.)
In these datacenters, the GEC values are exteremly small compared with the totalenergy consumed. Even with the Datacenter “A” with the highest GEC reported, the GEC
remains very low at approx. 0.34% of the total energy consumption. These results indicatethat the role of Green energies utilized in current datacenters is still insignificant.

34

3.2.3ITEE
The ITEE indicates the percentage of total rated capacity of the IT system comparedwith the total reated power of the IT system. Higher processing capacity per unit of a power
could result in higher ITEE number. The estimated ITEE range was approximately 1.0 to
4.42.
In this project, datacenters could meaured ITEE were 11 in Japan and 2 in overseas
locations. In Table 3-5, the ITEE results from January 2012 are shown. ITEE value will notchange unless the replacement of IT equipment will occur.
The measurement result indicated very scattered values between 0.16 and 3.95. In 3
datacenters, ITEEs were lower than 1.0. As for the reference, the project calculated the
ITEE for each datacenter based on the real IT equipment configuration (Server, Storageand Network). It is considered that the ITEE based on the IT equipment configuration could
indicate the strength and weakness of each configuration at each datacenter.
Table 3-5: ITEEs based on IT system configuration
System TypeBrokerage System 1

ITEE (Reference)
ITEE
Year of DC(January) Server Storage Network Establishment*
1.53

1.24

2.17

1.55

2005 -

0.74

0.63

1.99

1.21

2005 -

2.17

1.38

4.90

2.00

2005 -

Banking System

2.27

2.15

6.09

0.41

2005 -ASP System

1.42

1.64

3.01

0.10

2000 -

Cloud Service

2.61

3.77

4.02

1.60

Unknown

IDC Integrated
Housing Service

1.18

2.29

-

0.35

2000 -

Order Entry System

1.41

1.44

1.39

0.95

1980 -

Hosting Service

3.95

3.93

4.09

3.65

2005 -

Unknown

0.16

0.00

-

1.28

2005 -

Unknown

0.55

0.01

3.46

4.74

1990 -

Average

1.64

1.68

3.46

1.62

Brokerage System 2(Enterprise)
Brokerage Trading
SystemSingapore(September)
Vietnam(September)

1.64
1.16

(*1) coefficient ×total rated capacity (Server/Storage/Network) ÷ total rated power(Server/Storage/Network)(*2) Estimation based on PUE profile35

3.2.4ITEU
The ITEU indicates the percentage of total energy consumption of an IT systemversus the total rated power of the IT system (work unit). The estimated ITEUs wereapproximately 0.2 to 0.8.
In this project, datacenters could meaure ITEUs were 16 in Japan and 2 in overseas.
The ITEUs during the period between July 2010 and January 2011 were 0.41 - 0.48.The
maximum was at 0.58 and the minimum was at 0.26. All these numbers are within theestimated range for the ITEU.
The ITEU is an index to show the level of equipment usage in a datacenter. While
there were slight differences in these results among datacenters, no significant variations
were observed with most of datacenters with the comparison based on measured months(Table 3-6).
Table 3-6: ITEU / month based on the IT system type
AverageYear of DC
Establishment *Brokerage System 1

0.50

2005 -

Brokerage System 2 (Enterprise)

0.47

2005 -

Brokerage Trading System

0.41

2005 -

Banking System

0.51

2005 -ASP System

0.48

2000 -

Cloud Service

0.42

Unknown

IDC Integrated Housing Service

0.37

2000 -

Order Entry System

0.31

1980 -

IT outsourcing business system 1

0.46

2010 -

IT outsourcing business system 2

0.442000 -

Housing

0.38

2000 -

Hosting Service

0.43

2005 -ITEU measurement experiment 1

0.41

1995 -ITEU measurement experiment 2

0.47

2005 -

Unknown1

0.46

2005 -

Unknown2

0.60

1990 -

System Type

Average

0.44

Singapore

0.74

Vietnam

0.25* Estimation based on the PUE profile

36

The System type and average ITEU and other data for each datacenter weresummarized in the Table 3-9. However, it is not appropriate to consider these ITEU values
as the actual utilization ratio, because the rated power levels for IT equipment such asservers are designed to have larger margin than actual maximum value. Judging from themeasurement results to date, it is safe to consider that the averange ITEU is approximately
0.45 and the benchmark for the high unilization ratio ITEU is approximately 0.6.
2 overseas results are presented as reference only data, because their measurement
conditions were different from Japanese datacenter enviroment.

3.3

Examples of “Holistic Framework” Expression

In Section 3.2, the results were presented with focusing on the distribution ofindividual (sub) metrics. In this section, the description of the method will be shown tounderstand the characteristics of Energy efficiency in each datacenters based on theactual examples by the simultaneous presentation of four metrics with the use of a Holistic
Framework.
(1) This Example of a datacenter where improvement of efficiency is possible by the IT
hardware renewal
Fig. 3-7 indicates four metrics of a datacenter using a spider web chart. Thick solidline indicates the data for this datacenter and the dotted line indicates the average result
from the experimental measurement project.
While this datacenter is indicating the ITEU and PUE numbers higher than the
average, its ITEE is lower than the average. This result indicate that a possibility of an
example of High efficiency Operation. However, its IT equipment are somewhat older.
Thus, accordingly, replacement of the IT equipment is considered to be a promising
approach to improve the efficiency of this datacenter.

Data Center A
ITEU

ITEU =0.42

1.0

0.8
0.6

0.4

ITEE = 0.48

0.2

GEC GEC0.005 0.003= 0.000.0

1.0 2.0 3.0 4.0 5.0
ITEE
5.0
4.0
3.0
2.0
1.0
0.0

PUE

PUE =1.76
Fig. 3-7: Spider web chart of Holistic Framework of Datacenter A

37

(2) This Example of datacenter where improvement of operation efficiency is possible in
the future
The Datacenter B in Fig. 3-8 indicates lower ITEU while its ITEE and PUE are better
than the average compared with the above example of Datacenter A. Because ITEU is
closely related to the application used at each datacenter, it is rather difficult to make
immediate judgment. However, the data may suggest a possibility of the IT equipment of
this brand-new datacenter is designed to have larger capacity margins. If this is the case,
the ITEU value will become higher with increases in equipment utilization ratio at thisdatacenter.

Data Center B
ITEU
1.0

ITEU =0.37

0.8
0.6

0.4

GEC GEC0.005 0.003
=0.00

0.2
0.0

1.0 2.0 3.0 4.0 5.0
ITEE
5.0
4.0

ITEE
= 2.17

3.0
2.0
1.0
0.0

PUE

PUE = 1.60

Fig. 3-8: Spider web chart of Holistic Framework for Datacenter B(3) The Datacenter with improved efficiency in terms of both hardware and operation
Furthermore, Fig. 3-9 indicates an example of a datacenter, where all of the ITEU,
ITEE and PUE numbers are favorable values. This datacenter already demonstrates agood efficiency in terms of hardware and operation of IT equipment and its Datacenter
infrastructures. The only point requiring any further improvement is the introduction ofGreen energies. Because this Datacenter has not yet introduced a Green energy, further
improvement of the efficiency may be achieved by the Green energies.

38

Data Center C
ITEU
1.0

ITEU = 0.52

0.8
0.6

0.4
0.2

GEC
= 0.00GEC0.005

0.003

0.0

1.0 2.0 3.0 4.0 5.0
ITEE
5.0
4.0

ITEE = 2.27

3.0
2.0
1.0
0.0

PUE

PUE =1.63
Fig. 3-9: Spider web chart of Holistic Framework for Datacenter C(4) Chronological comparison of the energy efficiency
At last, Fig. 3-10 indicates an example of datacenter aging related changes in 2 years
period. Comparing the data between 2010 and 2011, improvements of the ITEU and PUE
are observed, while there is no significant change in ITEE. This result indicates that afurther improvement of efficiency is could be performed in terms of the facility and IT
equipment, while keeping the level of hardware efficiency for IT equipment could bemaintained.
In this case, the use of the Holistic Framework will allow the Datacenter
owner/operator to track the outcome of all efforts for improving the datacenter efficiency.

39

Data Center D- Comparison between FY2011 and FY2010 ITEU
1.0

0.8
0.6

0.4
0.2

GEC0.005

0.003

0.0

1.0 2.0 3.0 4.0 5.0
ITEE
5.0
4.0
3.0
2.0
1.0
0.0

PUE

Green: FY2010 (Sep), Blue: FY2011 (Sep)

Fig. 3-10: Periodic Spider web chart of Holistic Framework for Datacenter D

40

4.

Summary

To address the recent increase of Energy consumption in datacenters, the GIPC has
promoted the development of metrics to evaluate the energy efficiency and the evaluation
framework for the entire datacenter, which is called “DPPE”. In this development, the GIPC
tried to establish the metrics which could evaluate not only the datacenter infrastructures,
but also to improve the IT efficiency and introduction of the Green energy source.
By sub categorizing the efficiency target of the entire datacenter into four metrics tocompose the energy efficiency, it was possible to determine the efficiency in eachcategories and developed effective measures to improve the Energy efficiency.
Furthermore, while promoting the development of these metrics, the GIPC
investigated the usages of these metrics by implementing the Energy efficiency
experimental measurements at more than 20 datacenters in Japan and overseas
locations.
This research revealed the current status of the Japanese domestic datacenters. Also,by the use of the Holistic Framework with Four metrics, characteristics of the energy
efficiency in each datacenter were determined. In addition, the GIPC demonstrated thatthe analysis could guide each datacenter for which specifics to focus for the effort ofefficiency improvement. The GIPC consider that the improvement of Energy efficiency for
entire datacenter is feasible, and without any limitation of the scope to infrastructures by
the usage of this framework. The GIPC is planning to promote this approach for further
improvement of the metrics as the future work.

Acknowledgement
The GIPC would like to express the deep appreciation for the parties involved in theInternational Global Harmonization activities including; The Green Grid, U.S. EPA, U.S.
DOE, EU and EC - EU Code of Conduct, and BCS - British Computer Society for theirelaborate advice in developing the metrics for the datacenter energy efficiency and,particularly, in reviewing the actual utilization in a global scale.
With regards to the sophistication of the PUE measurement guideline, the GIPC arevery thankful to the great contribution of the JDCC – Japan Data Center Council. For themeasurement project in 2010 and 2011, more than 20 datacenter operators participated in
the project. The GIPC appreciate their cooperation.
For the activities of metrics development, Global Harmonization and International
Standardization in general, enormous supports were extended by Information andCommunication Electrics Division Team of Commercial and Information Policy Bureau,
Japanese Ministry of Economy Trade and Industry - METI.
And at last, all of these supports were essential to the accomplishment of this project.

41