ISO/IEC TS 8236-1:2025

Information technology - Provisioning, forecasting and management - Part 1: Data centre IT equipment

This document specifies a standardized method of optimizing IT provisioning within data centres by utilizing KPIs that enable the development of IT profiles for individual systems or platforms. The combination of the system and platform KPIs are used to establish an IT provisioning profile, establishing standard forecasting methods to optimize data centre resource effectiveness. This document: a) defines a method for identifying benchmarks and trends in IT provisioning; b) provides capability assessment/indicators of IT equipment over infrastructure life cycle, including preparatory, commissioning, expansion/contraction and/or retirement of IT equipment; c) provides a framework to establish IT provisioning forecast; d) provides a framework for IT provisioning output to be used as input to facility provisioning of space, power and cooling capacity requirements (see ISO/IEC TS 8236-2).

Technologies de l'information — Approvisionnement, prévision et gestion — Partie 1: Équipement informatique des centres de traitement de données

General Information

• Status: Published
• Publication Date: 16-Sep-2025
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 39 - Sustainability, IT and data centres
• Drafting Committee: ISO/IEC JTC 1/SC 39 - Sustainability, IT and data centres
• Current Stage: 6060 - International Standard published
• Start Date: 17-Sep-2025
• Due Date: 22-Jun-2025
• Completion Date: 17-Sep-2025

Overview

ISO/IEC TS 8236-1:2025 - Information technology - Provisioning, forecasting and management - Part 1: Data centre IT equipment - specifies a standardized method to optimize IT provisioning in data centres using KPIs. The Technical Specification defines how to develop IT provisioning profiles for systems and platforms, benchmark trends, assess equipment capability throughout the infrastructure lifecycle, and produce forecasts that feed facility planning for space, power and cooling.

Key topics

• KPI-based provisioning: standardized indicators to profile systems, platforms and combined IT provisioning profiles (DCitP).
• Benchmarking and trending: methods to identify historical and projected provisioning trends to support long-term planning.
• Lifecycle assessment: capability indicators for preparatory, commissioning, expansion/contraction and retirement phases.
• Forecasting framework: structured approach to create IT provisioning forecasts that inform facility capacity decisions.
• Platform coverage: guidance across IT platforms - compute, storage and network - including emerging topics such as liquid-cooled IT platforms and thermal considerations (e.g., thermal design power).
• Reporting and integration: reporting templates for DCitP and how to integrate IT provisioning output with facility provisioning (DCfP).
• Financial planning: application of provisioning profiles to forecast IT CAPEX and OPEX across in-house, colocation, managed and public cloud models.
• Practical examples: informative annexes provide provisioning and technology-refresh examples to illustrate application.

Applications

• Capacity planning: align IT growth profiles with long-lived facility systems (data centre facilities often have 10–25 year lifecycles vs. 3–5 year IT lifecycles).
• Facility-design optimization: use IT provisioning KPIs to calculate design power usage effectiveness (dPUE) more accurately than arbitrary IT load estimates.
• Migration and refresh planning: assess space, power and cooling impacts when moving to higher-density or liquid-cooled equipment.
• Financial forecasting: estimate future CAPEX/OPEX for IT and facility services using standardized provisioning outputs.
• Vendor and provider coordination: harmonize expectations between IT teams, data centre facilities, colocation providers and cloud operators.

Who should use this standard

• Data centre architects and facility planners
• IT provisioning and operations teams
• Capacity planners and sustainability officers
• Colocation and managed service providers
• Financial planners modeling IT CAPEX/OPEX

Related standards

• ISO/IEC TS 8236-2 - Data centre facility provisioning (integrates DCitP outputs)
• ISO/IEC 30134-2 - dPUE (design power usage effectiveness)

Keywords: ISO/IEC TS 8236-1:2025, data centre IT provisioning, KPIs, forecasting, capacity planning, power and cooling, liquid cooling, dPUE, IT lifecycle, CAPEX, OPEX.

Standards Content (Sample)

Technical
Specification
ISO/IEC TS 8236-1
First edition
Information technology —
2025-09
Provisioning, forecasting and
management —
Part 1:
Data centre IT equipment
Technologies de l'information — Approvisionnement, prévision et
gestion —
Partie 1: Équipement informatique des centres de traitement de
données
Reference number
© ISO/IEC 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
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Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
© ISO/IEC 2025 – All rights reserved
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions, abbreviated terms and symbols . 1
3.1 Terms and definitions .1
3.2 Abbreviated terms .2
3.3 Symbols .3
4 Data centre IT provisioning . 4
4.1 Introduction .4
4.2 IT provisioning trends .4
4.3 IT provisioning forecast .5
4.3.1 General .5
4.3.2 IT provisioning for public cloud services .7
4.3.3 IT provisioning for data centre services .7
4.4 New data centre projects .7
4.5 Liquid cooled IT platforms .7
4.6 IT Platforms.8
4.6.1 General .8
4.6.2 Compute .8
4.6.3 Storage .9
4.6.4 Network .11
5 Data centre IT provisioning for technology refresh example .12
5.1 General . 12
5.2 Analyse available RUs . . 13
5.3 Analyse available power .14
6 Reporting of DCitP . . 14
7 Integration of DCitP with DCfP .15
8 Application of DCitP to forecast IT CAPEX and OPEX .16
8.1 General .16
8.2 In-house data centre CAPEX and OPEX .17
8.3 Colocation data centre services and OPEX .19
8.4 Managed data centre services CAPEX and OPEX .19
8.5 Public cloud services OPEX .21
Annex A (informative) Data centre IT provisioning example .23
Annex B (informative) Data centre IT provisioning for technology refresh example.53
Bibliography .55

© ISO/IEC 2025 – All rights reserved
iii
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical activity.
ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations,
governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html.
In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 39, Sustainability, IT and data centres.
A list of all parts in the ISO/IEC 8236 series can be found on the ISO and IEC websites.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.

© ISO/IEC 2025 – All rights reserved
iv
Introduction
The global economy is increasingly more reliant on information and communication technologies and the
associated generation, transmission, compute and storage of digital data. All markets have experienced
growth in the digital data for social, educational, medical and business sectors. There are a wide variety of
data centres within private enterprise, shared/collocation and cloud service providers that meet the growing
demands of the digital data. The growth of this digital data will continue at a rapid pace with the development
of devices within the “Internet of Things” category, artificial intelligence applications and the increased
ability to generate and transmit data while mobile with the deployment of 5G technology. With the transition
from air-cooled information technology (IT) equipment to liquid-cooling IT equipment technologies, with
the advent of processors with higher thermal design power (TDP) characteristics, resulting in higher power
densities, it is also important to coordinate IT air-cooled vs liquid-cooled provisioning plans with facility
provisioning plans.
Compute and storage technologies and requirements continue to change rapidly. This creates challenges for
IT professionals who are responsible for planning for the provisioning compute and storage systems, and
the networks interconnecting the systems. Data centre IT systems and platform ecosystems typically have
lifecycles of 3 to 5 years. However, using traditional methods, IT provisioning planners are challenged to
identify provisioning requirements beyond even 1 year. This results in significant challenges for data centre
facility provisioning planners who are responsible for identifying requirements for data centre facility
systems that have life cycles of 10 to 25 years.
Data centre IT personnel responsible for provisioning IT systems are often unfamiliar with how the IT
systems impact facility planning. They are also often unfamiliar with the abundance of information that is
available to them that can help them to develop a holistic, long-term plan for provisioning data centres. This
has resulted in reactive provisioning. This has also impeded data centre facilities personnel responsible
for planning power, cooling and space provisioning. The data centre facilities personnel have little or no
knowledge of IT requirements or advanced notice of facility system capacities required to support IT
systems that are to be deployed within the data centre.
With this background, growth of digital data is inevitable, and the reactive planning status quo will result
in greater frustration for both the IT and facilities provisioning planners. There is therefore a need for a
method to benchmark and trend IT provisioning using standard indicators, processes, and reporting.
A data centre provisioning key performance indicator (KPI) will provide a method to profile future IT
system requirements over the life of the infrastructure. This method is based on the data centre’s current
IT applications and equipment platform, their expansion or contraction trends, and the impact of future
changes in technology network, compute and storage processing density and efficiency. This will help guide
designers and planners to optimize the capacity of the infrastructure to support the IT systems, providing
greater efficiency of the infrastructure resources implemented.
This document, in combination with ISO/IEC TS 8236-2, defines the benchmarking, trending and reporting
methodologies to be used to develop a holistic long-term provisioning plan.
The data centre provisioning KPI will be influential in guiding data centre designers and planners when
developing a design power usage effectiveness (dPUE) defined in ISO/IEC 30134-2. The data centre
provisioning KPI can be used in place of an arbitrary estimated IT load to develop the dPUE. The data centre
provisioning KPI will provide owners, designers and planners the opportunity to forecast IT loads using a
consistent methodology based on the provisioning profile.

© ISO/IEC 2025 – All rights reserved
v
Technical Specification ISO/IEC TS 8236-1:2025(en)
Information technology — Provisioning, forecasting and
management —
Part 1:
Data centre IT equipment
1 Scope
This document specifies a standardized method of optimizing IT provisioning within data centres by utilizing
KPIs that enable the development of IT profiles for individual systems or platforms. The combination of the
system and platform KPIs are used to establish an IT provisioning profile, establishing standard forecasting
methods to optimize data centre resource effectiveness.
This document:
a) defines a method for identifying benchmarks and trends in IT provisioning;
b) provides capability assessment/indicators of IT equipment over infrastructure life cycle, including
preparatory, commissioning, expansion/contraction and/or retirement of IT equipment;
c) provides a framework to establish IT provisioning forecast;
d) provides a framework for IT provisioning output to be used as input to facility provisioning of space,
power and cooling capacity requirements (see ISO/IEC TS 8236-2).
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO/IEC/TS 8236-2, Information technology — Provisioning, forecasting and management — Part 2: Data
centre facility
3 Terms, definitions, abbreviated terms and symbols
3.1 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC TS 8236-2 and the following apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1.1
data centre operations team
entity responsible for day-to-day and short-term tasks required to ensure IT services, or facility
infrastructure, are available and functional

© ISO/IEC 2025 – All rights reserved
3.1.2
drive density
volume of storage per physical size of storage device (e.g. areal density for hard disk drives)
3.1.3
IT platform
subset of equipment within an IT system to differentiate between various type of equipment, or a subset of
equipment across multiple IT systems grouped to associate various types of equipment, by physical, logical
or function characteristics, such as form factor, class, generation, application, etc.
3.1.4
IT system
set of one or more IT equipment providing network, compute or storage, or any combination of network,
compute or storage
3.1.5
single purpose system
IT system with a single operating system supporting a single application or function, non-virtualized
3.1.6
system
components grouped together to provide increased capacity or redundancy, or components integrated
together to provide required functionality
3.1.7
thermal design power
maximum amount of heat a CPU or GPU generates
3.1.8
virtual guest
operating system and the operating system’s integrated applications residing on a virtual host system
(commonly referred to as a virtual server), or bins and libraries integrated with applications residing on a
host system (commonly referred to as a container server)
3.1.9
virtual host
one or more physical IT devices (acting together for more capacity/ redundancy) as a host, supporting
multiple virtual machine guests
3.1.10
virtual to physical ratio
quantity of virtual guests on a virtual host expressed as a ratio, can be expressed as an average of total
quantity of virtual guests versus the quantity of virtual hosts all the guests are installed on
3.2 Abbreviated terms
For the purposes of this document, the abbreviated terms given in ISO/IEC TS 8236-2 and the following apply.
CMDB configuration management database
DCitP data centre IT provisioning
DCfP data centre facility provisioning
HPC high performance computing
LC-IT liquid cooled information technology
LFF large form factor server with a profile greater than two rack units

© ISO/IEC 2025 – All rights reserved
P IT platform, data illustrating historic trends or forecasts
RU rack unit
SaaS software as a service
SFF small form factor server with a profile of 2RU or less
TDP thermal design power
V2P quantity of virtual machine guests on a virtual machine host expressed as a ratio
3.3 Symbols
For the purposes of this document, the following symbols apply.
B business strategy variable
β
linear regression model
C life cycle
LC
D normal quantity of platform devices per rack or cabinet
R
m number of platform trends
R maximum rack units available within a cabinet for platform used to plan provisioning, excluding
MRU
any rack units required for other devices such as horizontal power strips, horizontal grounding
bars, network devices, patch panels, horizontal cable management, or blank rack units
n number of historic data points in a platform trend
P platform, data illustrating historic trends or forecasts
P platform where “i” represents the quantity of platforms within DCitP, “j” represents the quantity
i,j,k
of variables within the platform, and “k” represents the quantity of data points in the trend or
forecast
P physical servers per chassis for platform
PHY
NOTE Small and large form factor platforms have one server per chassis. A blade platform
typically has eight servers per chassis for full height servers or 16 servers per chassis for half
height servers.
R height of single chassis of platform, measured in rack units
RU
R total rack units
TRU
R consumed rack units within rack or cabinet
CRU
R rack units per platform device
RUD
s number of data points in a platform provisioning forecast
S refresh space
RS
T technology variable
t time
W weighting factor
© ISO/IEC 2025 – All rights reserved
y one or more variables used to establish a unique trend for the provisioning profile
η data centre IT provisioning
IT
4 Data centre IT provisioning
4.1 Introduction
The data centre industry has historically been challenged with a disconnect between IT and facilities
personnel responsible for designing, planning and implementing the systems for which they are responsible.
This challenge is primarily the result of both the IT and facility planners responsible for the physical systems
and infrastructure not participating, either by choice or by organizational structure and processes, in the
planning of the applications that the data centre is to support and the requirements of those applications.
The primary requirement in developing a data centre IT provisioning profile is to identify IT trends. The
profiles are developed by analysing and extrapolating historic trend data to estimate future requirements.
The trends may consist of growth or contraction. The trends for any data centre provisioning profile
consists of known trends, such as a known trend of increase in the amount of data required to be retained,
or variables, such as the change in storage drive density with each storage platform technology refresh over
a defined period of time.
The trends can identify a growth in infrastructure requirements such as when the data required to be
retained is growing at a rate that exceeds the rate in which storage drive densities increase with each
technology refresh, i.e. additional drives required at each technology refresh. The trends can also identify
a contraction in infrastructure requirements such as when the storage drive densities increase with each
technology refresh at a rate that exceeds the rate of increasing data required to be retained, i.e. fewer drives
required at each technology refresh.
IT trends are unique for each organization operating a data centre. IT trends can also be unique between
different data centres operated by a single organization. IT trends are influenced by an organization’s
tactic regarding new emerging technologies. IT trends can change rapidly with the development of new
technologies, however a specific organization’s deployment of new technology typically lag this rate
depending on their internal adoption rate of new technologies, such as bleeding edge, cutting edge, leading
edge or lagging. An organization may also have differing adoption rates across the various technologies and
platforms within their data centre.
IT trends and the resulting IT provisioning profiles are not intended to compare data centres. They are
intended to develop a profile that can guide designers and planners on future infrastructure capacity
requirements and energy consumption for a specific data centre based on the applications, IT systems and
platforms within the specific data centre.
4.2 IT provisioning trends
The level of detail and granularity required for IT trend analyse vary depending on the number of applications
supported within the data centre, the variation of IT systems and platforms supporting the applications,
and the availability of past trend data. This document is not intended to define specific trend data or units
of measure that are required for analyse. This document is intended to identify a methodology that can be
applied to any application, IT system, platform or data centre in developing IT provisioning profiles.
The IT trends are often characterized into one of three top-level platforms; compute, storage and network.
The use of lower-level platforms can increase the granularity of the IT equipment analysis, such as defining
platforms by physical attributes, logical attributes or functional attributes. A mix of the top-level and lower-
level platforms are used to provide further detail and compartmentalization of IT equipment or grouping of
associated IT equipment.
The compute, storage and network top-level and various lower-level platforms is discussed within this
document. However, many other platform characteristics exist and may be used to develop suitable data
centre IT provisioning profiles. Independent of how the platforms are characterized, all IT equipment

© ISO/IEC 2025 – All rights reserved
shall be included in a provisioning profile and an IT equipment component shall only be included in one
provisioning profile.
When determining how to catalogue all the IT equipment within the data centre into platforms, important
considerations include the ability to measure trend data, and the ability to incorporate how future changes
in technology or business strategies can be applied.
Each platform has one or more variables (y) that, when plotted as a function of time (t), provide unique
trend data for the platform.
y=f(t)
Each set of trend data consists of a sample set {(y , t ); k=−n, −n+1, …, 0} where n is the number of historic data
k k
points available and where n=0 is the current state. When gathering historic data, it is recommended that
a minimum of 3 years of data points be compiled for analysis. Reasonable data point intervals are weekly,
monthly or quarterly. Weekly intervals are recommended for data points that can be automatically gathered
and compiled with the use of automated tools. Quarterly data point intervals are not recommended as this
results in fewer data points to validate the DCitP on an annual basis, however quarterly intervals can be
appropriate for data points that require manual data gathering and compiling.
The quantity of variables that should be considered, measured and analysed vary depending on the platform
selected. The trend for a platform (P) with r variables and n historic data points is expressed as:
r
Py==kn−−,,n+…10,
{}∏ jk,
j=1
The IT trends of the data centre include multiple platforms (P) that represent IT equipment supporting
numerous applications. The platforms shall be selected such that all variables within each set of trend data
th
P shall have the same units of measure. The trend P marks the m IT equipment platform trend, and the
m
combination of all m platform trends encompasses all IT equipment supporting applications without any IT
equipment component included in more than one platform trend.
Thus, the combination of all m platforms (P) and all variables r within each platform, IT historic trends
represents the historic data centre IT provisioning and is expressed as:
m m r
 
 
ηIT==Py kn=− ,,−+n 10…,
 
i ij,,k
∑∑∏
 
i==11i j=1
 
4.3 IT provisioning forecast
4.3.1 General
For each historic trend data sample set {(y , t ); k=−n, −n+1, …, 0}, a linear regression analysis is a method
k k
that can be used to establish a trend line and provisioning forecast. An appropriate regression model should
be established for each trend. The model can consist of a simple linear regression or transformation linear
regression such as exponential, power, reciprocal or hyperbolic functions.
The analysis can apply a regression model that extends the provisioning forecast out 3, 5, 10 or more years.
The time intervals of the provisioning forecast should match the data point interval of the historic trend.
The provisioning forecast is represented by a line extending from the current value of the trend to s, where
s represents the product of the number of years to forecast and the quantity of time intervals within a year
for the provisioning forecast.

© ISO/IEC 2025 – All rights reserved
The representation of linear regression, whether it is a simple linear regression or a transformation linear
regression applied to each variable and each platform, within this document is expressed as:
yt=β() ks=…12,, ,
kk
As previously stated, when establishing a provisioning forecast for each specific platform, future changes
in technology or business strategies shall be considered and incorporated into the analysis as appropriate.
The estimated technology forecast is expressed as:
Tf= ()tk=…12,, ,s
kk
It is important to consider how the data centre IT operations team has incorporated past technology
advancements. Have they been quick to incorporate the latest technology advancement or do they lag the
general industry in incorporating the latest technology advancements? This operational characteristic
should be incorporated into the technology forecast function. For example, if the processing chip industry
is ready to release a new technology that significantly increases the number of cores available on a single
chip and the data centre operations team has a characteristic of lagging the general industry, the technology
forecast function has a delay on how the new technology impacts the IT provisioning forecast. If a data
centre operations team is quick to incorporate the latest technology advancements, the technology forecast
function includes an immediate step function that is applied to the IT provisioning forecast.
Examples of technology forecast to be considered are included in 4.5.
It is also important to consider changes in business strategies and how they impact the IT provisioning
forecast.
The estimated business strategy forecast is expressed as:
Bf= ()tk=…12,, ,s
kk
An organization can have a specific target to significantly increase their revenue beyond their normal
organic year-over-year growth, which can be achieved through various strategies such as offering new
services, expanding geographical territory or acquiring a competitor. Analysing how different strategies
impact the IT provisioning profile is a valuable tool to identify the impact on IT provisioning requirements.
An organization that plans to maintain their year-over-year growth without any additional significant
increase has a business strategy forecast function of 1 (i.e. B =1)
k
The IT provisioning forecast of a platform with r variables and s forecasted trend points, including the
impact of technology changes and business strategies, is expressed as:
r
Pt==β()TB ks12,,…,
jk,,jk jk,
{}∏
j=1
Thus, the combination of all m platforms (P) and all variables r within each platform, including the impact of
technology changes and business strategies, data centre IT provisioning forecast is expressed as:
m m r
 
 
ηIT==Ptβ TB k=12, ,,…,s
 () 
i ij,,ki,,jk ij,,k
∑∑∏
 
i==11i j=1
 
The challenge for any provisioning planner is to define appropriate platforms to compartmentalize or
group suitable IT equipment, and to select appropriate technology variables for each platform that can be
accurately measured and forecasted with a reasonable degree of accuracy.
The DCitP model should be reanalysed each year to compare previous years actual and forecasted values
to determine if the β(t ) regression method should be adjusted. The technology T and business strategy B
k k k
forecast models should also be reanalysed each year to compare previous years actual and forecasted values
to determine if they should be adjusted. This enables the data centre provisioning planner to regularly
validate the DCitP model and forecasted values.

© ISO/IEC 2025 – All rights reserved
4.3.2 IT provisioning for public cloud services
The IT provisioning forecast for a platform that is implemented on a public cloud service provider’s
infrastructure shall have the platforms and variables selected so that meaningful data is measured and
trends can be established for the provisioning forecast. Some examples of appropriate variables for platforms
implemented on a public cloud include; processing capacity (e.g. number of cores, amount of memory),
storage capacity (e.g. raw data), network capacity (e.g. throughput or bandwidth), I/O moved between each
system (compute, storage, internal and external network).
4.3.3 IT provisioning for data centre services
The IT provisioning forecast for a platform that is, or is to be, implemented within a data centre shall have
the platforms and variables selected so that meaningful data is measured and trends can be established for
the provisioning forecast. Some examples of appropriate variables for platforms implemented within a data
centre include characteristics that can be used to quantify the physical characteristics such as; space (e.g.
m or RUs), network capacity (e.g. number of ports, network throughput or bandwidth), power (kW) and
cooling (kW), data I/O moved between each system (compute, storage, network (internal and externally).
It is common for facility provisioning planners to use power density metrics such as W/m or W/rack.
However, this specification does not include, and discourages, developing these power density metrics
as part of IT provisioning. Power density metrics can be determined from the outputs of developing the
IT provisioning forecasts described in this specification. Power density metrics is discussed further in
ISO/IEC TS 8236-2.
4.4 New data centre projects
A DCitP provisioning plan should be developed when deploying data centre services in:
— a new data centre facility (either enterprise or colocation);
— a remodelled facility to serve as a data centre;
— an upgrade or expansion of an existing data centre;
— a migration of data centre services between enterprise, colocation or cloud service data centre models.
Organizations planning to deploy new or additional data centre services in any of these scenarios shall
establish key milestones within their DCitP provisioning plan. These milestones and the associated DCitP
provisioning profiles assist the data centre facility infrastructure planners validate the facility requirements.
The key milestones that shall be included in the DCitP provisioning plan include:
— identify “day one” migration start date;
— identify estimated duration of migration before full data centre services are operational.
4.5 Liquid cooled IT platforms
With the increase in power demands of high-density processing, it is possible that the ability to cool high
density IT equipment solely with air is no longer a viable solution. High-density chips continue to increase
their TDP values with each new product release. These high-density chips are typically integrated within IT
equipment that requires some type of liquid cooling IT equipment (LC-IT), with liquid entering and leaving
the chassis providing liquid cooling technology at the chip level. Liquid cooling technologies available
include:
— immersion cooling;
— direct to chip;
— spray cooling.
© ISO/IEC 2025 – All rights reserved
Even with liquid cooling at the chip level, some solutions utilize liquid cooling only on the CPU or GPU chips. The
memory and I/O components can still be air cooled within the same chassis as the liquid cooled CPU or GPU.
When liquid cooling solutions are deployed, the IT provisioning profile shall differentiate the trends for the
power load and heat load, between air-cooled components and liquid-cooled components. This is necessary
for the facility designers and provisioning planners to be able to provision adequate air-cooling vs liquid-
cooling system capacity.
4.6 IT Platforms
4.6.1 General
The IT trends are often characterized into one of three top-level platforms: compute, storage and network.
The use of lower-level platforms can increase the granularity of the IT equipment analysis, such as defining
platforms by physical attributes, logical attributes or functional attributes. A mix of the top-level and lower-
level platforms are used to provide further detail and compartmentalization of IT equipment or grouping of
associated IT equipment.
4.6.2 Compute
The compute top-level platform consists of IT equipment that provides computer processing to support
applications but can also include network or storage functions for converged systems.
The compute top-level platform can also be compartmentalized into lower-level platforms according to
physical attributes, such as:
— small form factor appliance servers (1RU or 2RU);
— large form factor appliance servers (>2RU);
— blade servers;
— large frame systems (e.g. mainframe, HPC).
The compute top-level platform can also be compartmentalized into lower-level platforms according to
logical attributes, such as:
— performance attributes measured in floating point operations per second (FLOPS), millions of
instructions per second (MIPS) or some other performance metric;
— converged solutions;
— hyperconverged solutions;
— single purpose systems (non-virtualized);
— systems with virtualized servers;
— systems with containerized servers.
The compute top-level platform can also be compartmentalized into lower-level platforms according to
functional attributes, such as:
— platforms supporting specific applications or services;
— platforms supporting specific internal organizational departments;
— platforms supporting specific external customers.
The compute platform trend data can be used to established data centre IT provisioning characteristics,
such as:
— quantity of virtual guests (virtual machines or containerized servers);

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— quantity of single purpose systems;
— quantity of FLOPs, MIPS;
— quantity of CPUs or cores;
— amount of memory.
The data centre IT provisioning compute variables that can assist in the development of platform trends and
forecasts include, but are not limited to:
— variations in applications or services supported by compute capacity;
— trend of virtual to physical ratio (V2P ratio):
— technology capability trends;
— data centre adoption trends.
— trend of cores per CPU:
— technology capability trends;
— data centre adoption trends.
— trend of cores or CPUs per server:
— technology capability trends;
— data centre adoption trends.
— trend of memory capacity per server:
— technology capability trends;
— data centre adoption trends.
Independent of the trend data or variables used, the output of DCitP shall enable the provisioning planner to
use the data to establish the quantity of forecasted physical servers and space or rack units required. A data
centre with few IT systems (e.g. data centre with a small quantity of IT cabinets) or few applications (e.g.
SaaS cloud service provider or hyper-scale data centre) generally have fewer data centre IT provisioning
platforms to analyse than a large enterprise data centre with many IT cabinets, IT systems and applications.
The trend data can typically be captured with a combination of management tools, such as:
— asset management tool;
— compute hardware remote access controller or management processor management tool;
— virtual host management tool;
— configuration management database (CMDB) management tool.
Refer to Clause A.1 for an example of a data centre IT provisioning compute trend and forecast.
4.6.3 Storage
The storage top-level platform consists of IT equipment that provides data storage to support applications.
The storage top-level platform can also be compartmentalized into lower-level platforms according to
physical attributes, such as:
— network-attached storage (NAS);
— large frame drive arrays (SAN);

© ISO/IEC 2025 – All rights reserved
— rackable systems vs stand-alone systems;
— hard drive vs solid state;
— tier classification of drives:
— tier 1: higher speed, lower drive density;
— tier 2: moderate speed and drive density;
— tier 3: lower speed, higher drive density.
The storage top-level platform can also be compartmentalized into lower-level platforms according to logical
attributes, such as:
— block level storage;
— file level storage;
— converged solutions;
— hyperconverged solutions.
The storage top-level platform can also be compartmentalized into lower-level platforms according to
functional attributes, such as:
— platforms supporting specific applications or services;
— platforms supporting specific internal organizational departments;
— platforms supporting specific external customers.
The storage platform trend data can be used to establish data centre IT provisioning characteristics such as:
— quantity of physical frames;
— quantity of physical chassis;
— quantity of physical drives;
— quantity of rack units (RUs);
— capacity of formatted storage.
The data centre IT provisioning compute variables that can assist in the development of platform trends and
forecasts include, but are not limited to:
— trend of drive density;
— trend of drive form (e.g. 3.5 in vs 2.5 in);
— trend of HDD vs SSD;
— trend of NAS vs SAN.
Independent of the trend data or variables used, the output of DCitP shall enable the provisioning planner
to use the data to establish the quantity of forecasted physical storage systems, and space or rack units
required.
The trend data can typically be captured with a combination of the management tools, such as:
— asset management tool;
— storage hardware remote access controller or management processor management tool;
— NAS or SAN management tool;
© ISO/IEC 2025 – All rights reserved
— CMDB management tool.
Refer to Clause A.2 for an example of a data centre IT provisioning storage trend and forecast.
4.6.4 Network
The network top-level platform consists of IT equipment that provides network interconnection to compute
and storage systems. Top-of-rack (ToR) network access switches for compute or storage systems are either
included in the network data trend analysis and provisioning forecast, or in the system the ToR switch is
providing access for (e.g. compute or storage data trend analysis and provisioning forecast). An analysis
should be conducted to determine how the ToR switches are accounted for since they share rack space with
compute and/or storage systems.
The network top-level platform can also be compartmentalized into lower-level platforms according to
physical attributes, such as:
— appliances with fixed ports;
— appliances with pluggable form factor modules (i.e. SFP, SPF+, QSFP, QSFP+, QSFP-DD, OSFP);
— chassis with modular network cards.
The network top-level platform can also be compartmentalized into lower-level platforms according to
logical attributes, such as:
— logical layer within the data centre architecture:
— edge layer;
— core layer;
— spine/distribution/aggregation layer;
— leaf/access layer.
— OSI layer.
The network top-level platform can also be compartmentalized into lower-level platforms according to
functional attributes, such as:
— switching or routing;
— acce
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