ISO/IEC TR 20913:2016

TECHNICAL ISO/IEC TR
REPORT 20913
First edition
2016-11-15
Information technology — Data
centres — Guidelines on holistic
investigation methodology for data
centre key performance indicators
Technologies de l’information — Centres de données — Lignes
directrices relatives à la méthodologie de recherche holistique pour
les indicateurs de performance clé du centre de données
Reference number
©
ISO/IEC 2016
© ISO/IEC 2016, Published in Switzerland
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ii © ISO/IEC 2016 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
4 Background and motivation . 2
4.1 General concept of holistic investigation method . 2
4.2 Usefulness of spider web chart methods for visualizing data centre KPIs . 3
4.3 Usefulness of aggregating data centre KPIs . 4
5 Spider web chart-based KPIs status observation method . 4
5.1 Principles for constructing a spider web chart using KPIs . 5
5.1.1 Selection of axis on a spider web chart . 5
5.1.2 Presentation of KPIs on axes . 5
5.2 Example of a holistic approach . 5
5.3 Example of holistic approach of data centre by use of a spider web chart . 6
6 Control chart method extending a basic spider web chart to observe the
operational status .11
6.1 Motivation for control chart method for energy efficiency monitoring .11
6.2 Control chart approach for energy efficiency monitoring .11
7 Considerations for applying holistic investigation methods .14
8 SWOT analysis results for holistic investigation methods .14
Bibliography .16
© ISO/IEC 2016 – 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. In the field of information technology, ISO and IEC have established a joint technical committee,
ISO/IEC JTC 1.
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).
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation on the meaning of ISO specific terms and expressions related to conformity
assessment, as well as information about ISO’s adherence to the WTO principles in the Technical
Barriers to Trade (TBT) see the following URL: Foreword - Supplementary information.
The committee responsible for this document is ISO/IEC JTC 1, Information technology, SC 39,
Sustainability for and by Information Technology.
iv © ISO/IEC 2016 – All rights reserved

Introduction
The ISO/IEC 30134 series defines key performance indicators (KPIs) for data centre resource
effectiveness. There are many aspects to be considered in order to improve data centre resource
effectiveness. As for resources, it may include not only energy, but also water and other natural
resources. As for data centre components, they include air conditioning, power supply, servers, storages,
and network equipment. However, it is difficult to include all aspects into one KPI, so multiple KPIs
are under development, which measure each aspects of resource effectiveness improvement. Resource
effectiveness improvement in each aspect will be performed by measuring each KPI. On the other hand,
there is a need to observe the state and trend of data centre as a whole, or holistically, by monitoring
multiple KPIs in a single view. Analysis of the KPIs from the overall perspective is also referred to
as a holistic investigation method. This document describes a spider web chart-based method and
control chart method extending the functionality of the conventional spider web chart for viewing and
analysing KPIs for data centre resource effectiveness. It also investigates considerations for applying
holistic investigation methods to resource effectiveness evaluation of multiple data centre KPIs. The
usefulness and applicability of holistic methods are discussed using a SWOT analysis. The methods
described in this document are intended for analysis and continuous improvement of a specific data
centre and not for comparing different data centres.
© ISO/IEC 2016 – All rights reserved v

TECHNICAL REPORT ISO/IEC TR 20913:2016(E)
Information technology — Data centres — Guidelines on
holistic investigation methodology for data centre key
performance indicators
1 Scope
This document describes backgrounds, motivation, and general concept of holistic methodology for data
centre key performance indicators (KPIs) to investigate the status of KPIs. It discusses the usefulness of
holistic investigation methodology in terms of aggregating a KPI across different contexts, aggregation
of two or more KPIs within a single context, aggregation of two or more KPIs across multiple contexts,
and aggregation of the multiple KPIs into a single indicator. This document presents a conventional
spider web chart-based data centre KPIs status observation method and a control chart method
including upper bound and lower bound of the operational status of KPIs. This document presents
SWOT analysis results for both methodologies. The methods described in this document are aimed at
the self-monitoring of a data centre, not comparison among data centres.
Specifically, this document
a) describes backgrounds, motivation, and general concept of holistic investigation methodology for
data centre KPIs,
b) analyses the usefulness of holistic investigation methodology for aggregating KPIs,
c) describes a spider web chart-based KPIs status observation method and a control chart extending
spider web chart to observe the operational status of KPIs,
d) describes alternative and/or additional methods of representing dissimilar KPIs to track holistic
resource effectiveness of the data centre, and
e) presents SWOT analysis results for holistic investigation methods described in this document.
2 Normative references
There are no normative references in this document.
3 Terms, definitions and abbreviated terms
3.1 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— IEC Electropedia: available at http://www.electropedia.org/
— ISO Online browsing platform: available at http://www.iso.org/obp
3.1.1
holistic investigation method
data centre resource effectiveness investigation method considering multiple key performance
indicators
© ISO/IEC 2016 – All rights reserved 1

3.1.2
spider web chart
chart that consists of multiple performance indicators which are set in a circle like a spider web
3.2 Abbreviated terms
IT Information Technology
ITEEsv IT Equipment Energy Efficiency for Servers
ITEUsv IT Equipment Utilization for Servers
KPI Key Performance Indicator
PUE Power Usage Effectiveness
REF Renewable Energy Factor
SWOT Strength Weakness Opportunity Threat
4 Background and motivation
4.1 General concept of holistic investigation method
Improving the resource effectiveness and carbon footprint of a data centre requires the monitoring
and analysis of multiple KPIs. ISO/IEC JTC 1/SC 39 has determined that it is impractical to aggregate
multiple KPIs to determine the overall energy effectiveness of a data centre. There is a need to observe
the state and trend of multiple KPIs in a single view.
With any performance indicator, it is necessary to understand the expected upper and lower limits and
general behaviour of the performance indicator. There are typically two approaches that are applicable
to holistic investigation of data centre KPIs:
— Engineering/modeling method: This method has been used to establish baseline performance. This
methodology requires the development of an optimized economic and engineering model based
on creating an idealized benchmark specific to each utility — incorporating the topology, demand
patterns, and population density of the service territory. Typical limitations of this approach are as
follows: the engineering models that support it can be very complicated, and the structure of the
underlying components relationships can be obscured through a set of assumed coefficients used in
the optimization process.
— Performance benchmarking method: This method includes a set of specific performance
measurement indicators, such as volume billed per worker, consumed energy per product, quality
of service (continuity, water quality, complaints), coverage, and key financial data. Usually, these
indicators are presented in ratio form to control the scale of operations. These partial measures are
generally available and provide the simplest way to perform comparisons: trends direct attention
to potential problem areas.
Among the methods mentioned above, the performance benchmarking method is useful for evaluating
the resource efficiency of data centres because ISO/IEC JTC 1/SC 39 is offering a selection of energy
effectiveness KPIs. The performance benchmarking method may be further categorized into two types:
performance indicator-based methods and chart-based methods.
— Performance indicator-based methods: In this category, the performance of the target is evaluated
by developing performance indicators for the target. For example, Hz for CPU and bytes for storage
are typical performance indicators. This category allows accurate performance evaluation and
comparison among targets, if the performance indicators are defined. Typical limitation of this
approach is that it is difficult to compare the evaluation results if performance indicators belong to
different dimensions with different units.
2 © ISO/IEC 2016 – All rights
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