ISO/IEC TR 30132-1:2016
(Main)Information technology — Information technology sustainability — Energy efficient computing models — Part 1: Guidelines for energy effectiveness evaluation
Information technology — Information technology sustainability — Energy efficient computing models — Part 1: Guidelines for energy effectiveness evaluation
ISO/IEC TR 30132-1:2016 establishes guidelines for improving the energy effectiveness for computing models. Specifically, this document provides - a reference computing model for evaluating end-to-end energy effectiveness, - a holistic framework for evaluating the applicability of energy effectiveness improving technologies, and - guidelines for evaluating energy effectiveness.
Technologies de l'information — Disponibilité des technologies de l'information — Modèles informatisés à efficacité énergétique — Partie 1: Lignes directrices pour l'évaluation de l'effectivité énergétique
General Information
Standards Content (Sample)
TECHNICAL ISO/IEC TR
REPORT 30132-1
First edition
2016-09-15
Information technology —
Information technology sustainability
— Energy efficient computing
models —
Part 1:
Guidelines for energy effectiveness
evaluation
Technologies de l’information — Disponibilité des technologies de
l’information — Modèles informatisés à efficacité énergétique —
Partie 1: Lignes directrices pour l’évaluation de l’effectivité
énergétique
Reference number
ISO/IEC TR 30132-1:2016(E)
©
ISO/IEC 2016
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ISO/IEC TR 30132-1:2016(E)
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ISO/IEC TR 30132-1:2016(E)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 1
5 Reference computing model for end-to-end energy effectiveness evaluation .2
5.1 Overview of computing models . 2
5.2 Reference computing model and energy effectiveness evaluation . 4
6 Holistic framework for evaluating the applicability of energy effectiveness
improving technologies . 6
6.1 Motivation . 6
6.2 Overview of holistic framework . 7
6.3 Considerations for evaluating the applicability . 7
6.4 Examples of energy effectiveness evaluation . 7
7 Guidelines for determining the energy effectiveness of a computing model .11
Annex A (informative) State of affairs for improving energy effectiveness of computing systems .12
Annex B (informative) Survey for calculating power consumption of the components in
computing models.19
Bibliography .21
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ISO/IEC TR 30132-1:2016(E)
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, Subcommittee
SC 39, Sustainability for and by Information Technology.
A list of all parts of the ISO/IEC 30132 series can be found on the ISO website.
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ISO/IEC TR 30132-1:2016(E)
Introduction
The world is experiencing explosive growth of data from mobile client devices, cloud services, social
networks, online television, the Internet of things, big data and from traditional enterprise computing.
The growth of data has been accompanied by a growth in the energy usage and carbon footprint of
IT along with increased costs. Much research has been performed regarding energy management for
the last two decades, most focusing on the evaluating and improving energy efficiency of individual
components or systems such as processors, memory, wireless networks base stations, laptops,
supercomputers, data centres, handheld devices and so on. However, several disparate systems,
or systems of systems, collectively use energy to accomplish a given task and satisfy service-level
expectations. Consider, for example, someone who takes a photo with a smartphone and posts it to
a social network for their friends to view. Taking and transmitting the photo consumes energy from
the smartphone while the data transfer, processing and storage consumes energy too. Likewise, when
friends view the photo, that activity will consume additional energy. To improve energy effectiveness, it
is necessary to consider the end-to-end energy use of a task or service involving multiple systems.
The ISO/IEC 30132 series provides guidelines for the end-to-end evaluation of energy effectiveness of
a reference computing model and suggestions for determining the energy effectiveness of a computing
model. This document comprises guidelines for energy effectiveness evaluation, including a reference
computing model that includes end-to-end data transfer, processing and storage.
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TECHNICAL REPORT ISO/IEC TR 30132-1:2016(E)
Information technology — Information technology
sustainability — Energy efficient computing models —
Part 1:
Guidelines for energy effectiveness evaluation
1 Scope
This document establishes guidelines for improving the energy effectiveness for computing models.
Specifically, this document provides
— a reference computing model for evaluating end-to-end energy effectiveness,
— a holistic framework for evaluating the applicability of energy effectiveness improving
technologies, and
— guidelines for evaluating energy effectiveness.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 13273-1 and the
following 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 https://www.iso.org/obp/
3.1
energy effectiveness
end-to-end total amount of data transferred, processed and stored per unit energy of a computing model
4 Abbreviated terms
ARP address resolution protocol
BNG broadband network gateway
DHCP dynamic host configuration protocol
DSL digital subscriber loop
DSLAM DSL access multiplexer
FTTN fibre-to-the-node
FTTP fibres-to-the-premises
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HSPA high speed packet access
ICMP internet control message protocol
ICT information and communication technology
ISP internet service provider
NIC network interface card
OLT optical line terminal
ONU optical network unit
PON passive optical network
PtP point-to-point
QoS quality of service
UMTS universal mobile telecommunications system
WDM wavelength-division multiplexing
5 Reference computing model for end-to-end energy effectiveness evaluation
5.1 Overview of computing models
This subclause provides a survey of trends for various computing models.
In the traditional client-server computing model, clients are connected to servers via networks such
as the Internet. In this paradigm, a server is a computer system that selectively shares its resources,
whereas a client is a computer that initiates requests to a server in order to use its resources.
However, the emergence of new computing models such as cloud computing and the Internet of things,
along with new devices such as mobile phones, tablets, wearables and Internet connected sensors
introduce new considerations for both computing models and determining energy effectiveness.
Additionally, data is now transported over traditional wired networks and over high- and low-speed
wireless networks. Some client devices only support wireless networks.
Energy effectiveness has traditionally been viewed on a per device-category basis, but now it is
important to look at energy effectiveness from an end-to-end perspective that includes all the devices,
sub-systems and software that delivers a given set of functionalities (also known as a service).
New paradigms for the creation and use of data also affect energy effectiveness end-to-end. For
example, many users have their data on multiple devices and synchronization between client devices
and servers has become common practice. This means the same data may reside on multiple devices.
On the other hand, some applications retrieve and display data on the client device only as needed. This
scenario increases loads on networks, servers and storage, which may increase energy consumption.
The shift to mobile client devices and the shift to cloud computing, along with increases in the total
number of connected devices have driven a dramatic increase in the number of data centres and faster,
higher capacity networks with a corresponding increase in energy use, while client devices continue
to improve their energy effectiveness. Customer expectations for highly available, responsive services
may cause servers, storage and networking equipment to stay at high power states longer, possibly
conflicting with power management schemes and energy effectiveness goals. New technologies such as
push notifications also increase data and energy use across systems.
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Therefore, energy effectiveness assessments should identify all of the energy consuming components
in the computing model. The energy effectiveness of networks is calculated using manufacturers’
data on equipment energy consumption for a range of typical types of equipment in networks. The
manufacturers’ data can include the amount of energy consumption in various states of equipment
such as idle, active and fully utilized state. This approach enables an overall model of network power
consumption to be constructed and provides a platform for predicting the growth in power consumption
[23]
as the number of users and access rate per user increase .
Figure 1 shows a high level representation of the network model of the Internet. In Figure 1, the Internet
is segmented into three major components: access network, metro network and core network with data
centres. The model is an abstract representation of the Internet and, as such, does not include much of
the fine detail of the Internet’s true structure and topology. The model does account for the typical hop
[24]
count for packets that traverse the Internet .
The refinement to include a more realistic representation of the Internet’s topology is ongoing. The
access network connects individual users to their local exchanges. Some of the typical access network
technologies, such as digital subscriber loop (DSL) to deliver packets through fixed-line telephone
service, fibres-to-the-premises (FTTP) installations to provide shared passive optical network (PON)
or a point-to-point (PtP) Ethernet connection. In a PON, a single fibre from the network node feeds one
or more clusters of users by using a passive optical splitter. An optical line terminal (OLT) is located at
the local exchange to serve many access modems or optical network units (ONUs) located at each user.
ONUs communicate with the OLT in a time division multiplexing, with the OLT assigning time slots
to each ONU based on its relative demand. In a PtP access network, each ONU is directly connected
to the local exchange with a dedicated fibre to the exchange. In areas where the copper pairs are in
good condition, a fibre-to-the-node (FTTN) technology may be used. This technology uses a dedicated
fibre from the local exchange to a DSL access multiplexer (DSLAM) located in a street cabinet close to
a cluster of users. A high-speed copper pair technology, such as very-high-speed DSL, is used from the
cabinet to the users. In areas where copper and fibre are not available or feasible, wireless can provide
Internet access. Technologies for the wireless access include WiMAX, high speed packet access (HSPA),
and universal mobile telecommunications system (UMTS). For wireless access, a wireless modem,
located in the user, communicates with a local wireless base station which, in turn, is connected to the
[23]
central office .
The central offices in a city are connected to each other and to other cities via the metro/edge network.
This network also provides connection points for Internet service providers (ISPs). The metro and edge
network serves as the interface between the access and core networks. The metro and edge network
includes edge Ethernet switches, broadband network gateway (BNG) and provider edge routers. Edge
Ethernet switches concentrate traffic from a large number of access nodes uplink to two or more BNG
routers. The edge switch connects to two or more BNG routers to provide redundancy. The BNG routers
perform access rate control, authentication and security services, and connect to multiple provider
[23]
edge routers to increase reliability. The provider edge routers connect to the core of the network .
The core network comprises a small number of large routers in major population centres. These core
routers perform all the necessary routing and also serve as the gateway to neighbouring core nodes.
The core routers of any one network are often highly meshed, but only have few links to the networks
of other providers. High-capacity wavelength-division multiplexed (WDM) fibre links interconnect
[23]
these routers and connect to networks of other operators .
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[23]
Figure 1 — High-level network structure for the Internet
5.2 Reference computing model and energy effectiveness evaluation
Since there are many components in modern computing models and each has unique energy
effectiveness characteristics, it is difficult to calculate the energy consumption of services. Therefore,
this document uses a simplified generic reference computing model consisting of client devices,
network equipment and data centre equipment as shown in Figure 2. It is assumed that data centre has
computing resources such as server and storage. This document considers the following assumptions
for the simple evaluation of energy effectiveness of computing models.
— This document evaluates energy effectiveness of computing models from the view point of end-to-
end data creation, processing, storage, consumption and sharing.
— The model also assumes a data-oriented scenario where data moves back and forth between client
devices and data centres.
This document considers following components for evaluating end-to-end energy effectiveness while
considering application’s characteristics:
— client devices;
— network equipment;
— data centre equipment.
For survey for calculating power consumption of the components in computing models, see Annex B.
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Figure 2 — Simplified generic reference computing model for energy effectiveness evaluation
From the perspective of computing the overall energy effectiveness, this document takes a very high-
level macroscopic view. At this level, it is considered that the transport network consists of multiple
wired and wireless network equipment. Client devices can take many forms including wearable
technologies, sensors and controls. It is noted that this document considers network layer equipment
in estimating the energy effectiveness of networks because the detail information about networking
equipment below network layer is difficult to obtain due to security and confidentiality. Thus, internal
network equipment are not taken into account. In case of data centre, border routers that connect
data centre to the Internet are taken into account. The internal routers within a data centre are not
considered. However, if the internal network information of clients, transport networks and data centre
are available, those information may be utilized in order to estimate the energy effectiveness more
accurately. Therefore, the end-to-end energy effectiveness evaluation can be performed by calculating
the energy effectiveness of the following components:
— client devices;
— transport network: in Figure 2, it is assumed that transport network includes access, metro/edge
and core network;
— data centre equipment.
The individual architectures that make up the high level components in the model are much more
complicated and the corresponding details and complexities are not within the scope of this document.
The energy consumption (watt-hours) of the end-to-end path in the reference computing model can be
calculated as follows:
EE=+ EE+
∑∑∑
E2Eclient_device,ni et_equip,djkatacentre_equip,
i jj k
The energy consumption measurement of components in the reference computing model can be
performed as follows.
— Basic assumption: each component is equipped with monitoring function, which can monitor and
measure throughput level of target equipment. When the monitoring value reaches a designated
threshold value, the amount of consumed energy is measured.
— Client devices and data centre equipment: monitoring function can measure the amount of power
consumption at the offered load to the equipment. Calculation of the energy consumption on client
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device or data centre equipment can be performed by using measurement time, T, and power
consumption upon offered load as follows:
T
EP= offered_load
()
∑
equipequip
0
— Network equipment: network throughput can be calculated as doing sum of traffic load entered into
each network equipment. For this, the monitoring function can collect information about network
throughput such as offered load to measure the power information of the equipment. When the
network throughput reaches at target threshold value for power measurement, the monitoring
function gathers the information upon traffic load on each node and calculates the energy
consumption at the target traffic load. The total amount of power consumption on the network
equipment can be calculated by using the information upon each traffic load. The consumed power
on total network equipment can be the sum of the consumed power on each node. The power
measurement can be done depending on different throughput states (e.g. 10 %, 20 %, etc.). When
the value of traffic load is known, any network equipment can calculate power consumption value
of each node according to traffic load. It is noted that if calculating power consumption of each
network equipment, a practical way to calculate the network energy consumption may be to take
each segment, measure the total energy consumed over T, divide that by the total number of bytes
of data that traversed that segment over T and then multiply by the number of bytes of data for the
service that traversed that network segment over T.
T
EP= traffic_load
()
∑
net_equipnet_equip
0
Since the energy effectiveness evaluation of reference computing model only deals with end-to-end
data scenario, the end-to-end energy effectiveness can be calculated as follows.
Comparing product metrics allow consumers, enterprises and carriers to add energy effectiveness to
purchase criteria. A straightforward way to estimate the energy effectiveness of a network or telecom
system is to normalize its energy consumption to the amount of transmitted data in the test.
Let B be the end-to-end total amount of data transferred, processed and stored during the
E2E
measurement time, T, in the reference computing model and η be the overall energy effectiveness
E2E
calculated. Therefore, the end-to-end network energy effectiveness is calculated by dividing the total
amount of data, i.e. B by the total energy consumption of the computing model.
E2E
B
E2E
η =
E2E
E
E2E
6 Holistic framework for evaluating the applicability of energy effectiveness
improving technologies
6.1 Motivation
As the energy effectiveness of computing systems that use a large amount of electricity becomes an
important issue to network providers and operators, there is an increasing demand for developing
standards for evaluating and assessing the effectiveness and applicability of various energy
effectiveness improving technologies. This clause defines effective and holistic framework for assessing
the energy effectiveness improving technologies.
The rapid increase of energy consumption of ICT products accelerates the development of energy
efficient technologies. However, since energy efficient technologies in different dimensions may cause
unexpected side effects in total energy effectiveness of a computing system, it becomes necessary to
develop effective methods for understanding the inter-relationship among multiple energy efficient
technologies and evaluating energy effectiveness based on holistic view. This subclause also provides
motivation for holistic framework.
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For state of affairs for improving energy effectiveness of computing systems, see Annex A.
6.2 Overview of holistic framework
This subclause provides an overview of holistic framework. When evaluating energy effectiveness
of computing models, it is generally necessary to consider multiple energy effectiveness metrics
due to the heterogeneity of components comprising the computing models. For example, the energy
effectiveness of computing node may be expressed in terms of CPU speed over consumed energy,
whereas the effectiveness of network node may be expressed in terms of network bandwidth over
consumed energy. Holistic energy effectiveness evaluation means that the evaluation is performed
considering multiple energy effectiveness indices simultaneously. The effectiveness indices may be
homogeneous or heterogeneous among them. The holistic methods allow easy comparison of holistic
energy effectiveness among multiple computing models or temporal trend. Also, it is possible to
understand how the change of values in energy effectiveness metrics contributes to the overall energy
effectiveness. For example, if the operator of a data centre wants to increase entire data centre energy
effectiveness by double, it is possible to calculate how much improvement should be performed for each
metric. During the holistic evaluation, it is possible to consider the characteristics of each computing
model when estimating the holistic energy effectiveness using multiple energy effectiveness metrics. It
is noted that depending on the service level objectives and agreements between customers and service
providers, the performance and availability of services may vary. These service level objectives may
affect the energy consumption of customer services. Thus, it is desirable to take account of customer
expectations in order to provide more accurate energy effectiveness estimation.
6.3 Considerations for evaluating the applicability
This subclause provides considerations for developing and evaluating the applicability of energy
efficient technologies for computing systems during sustainable ICT product during use stage of
life cycle. In the computing models, energy is consumed during computing, data transport or data
storing operations. In other words, not only data transport, but also data manipulation causes energy
consumption. Moreover, the rapid deployment of mobile devices enables users to use multiple personal
devices and maintain synchronized data among the devices. Therefore, user’s activities can cause more
effects to the energy consumption of ICT infrastructure than legacy environments. Current computing
models impact energy requirements in multiple systems. Also, use of enterprise as computing, data
source and maintenance requires data management across multiple systems. Low power, low capability
end devices require increases in enterprise services and availability on resilient networks, servers and
storage, and low capability end devices increases data centre and network energy requirements.
Therefore, the evaluation of computing models needs to consider the following issues:
— consideration for application’s impact on ICT infrastructure in terms of data transport, computation
and data storing;
— consideration for application’s characteristics when executing the application. For example, users
may execute application in a local node that has relatively, but does not require data transport over
network or may execute the application at remote cloud node that has high performance, but needs
data transmission over networks.
6.4 Examples of energy effectiveness evaluation
As investigated in Clause 5, the energy effectiveness of end-to-end communication is represented as
a ratio of total amount of data transferred, processed and stored to total energy consumption. In this
subclause, it is described how to evaluate end-to-end energy effectiveness by using examples. Figure 3
shows an example scenario for end-to-end energy effectiveness evaluation. In this example, a client
device sends data traffic to the server in a data centre, and the sever stores the received data to an
external storage systems.
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Figure 3 — Example scenario for end-to-end energy effectiveness evaluation
This document considers the following assumptions.
B = is the amount of data traffic (Mbytes) that client device transmits to the server in the data centre.
...
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