Use Cases for Energy Efficiency Management
draft-ietf-green-use-cases-01
| Document | Type | Active Internet-Draft (green WG) | |
|---|---|---|---|
| Authors | Emile Stephan , Marisol Palmero , Benoît Claise , Qin Wu , Luis M. Contreras , Carlos J. Bernardos , Xinyu Chen | ||
| Last updated | 2026-01-26 (Latest revision 2026-01-22) | ||
| Replaces | draft-stephan-green-use-cases | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | (None) | ||
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draft-ietf-green-use-cases-01
Getting Ready for Energy-Efficient Networking E. Stephan
Internet-Draft Orange
Intended status: Informational M. Palmero
Expires: 26 July 2026 Individual
B. Claise
Q. Wu
Huawei
L. M. Contreras
Telefonica
C. J. Bernardos
Universidad Carlos III de Madrid
X. Chen
China Mobile
22 January 2026
Use Cases for Energy Efficiency Management
draft-ietf-green-use-cases-01
Abstract
This document groups use cases for Energy efficiency Management of
network devices.
Discussion Venues
Source of this draft and an issue tracker can be found at
https://github.com/emile22/draft-ietf-green-use-cases
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at
https://emile22.github.io/draft-ietf-green-use-cases/draft-ietf-
green-use-cases.html. Status information for this document may be
found at https://datatracker.ietf.org/doc/draft-ietf-green-use-
cases/.
Discussion of this document takes place on the Getting Ready for
Energy-Efficient Networking Working Group mailing list
(mailto:[email protected]), which is archived at
https://mailarchive.ietf.org/arch/browse/green/. Subscribe at
https://www.ietf.org/mailman/listinfo/green/.
Source for this draft and an issue tracker can be found at
https://github.com/emile22/draft-ietf-green-use-cases.
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Status of This Memo
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This Internet-Draft will expire on 26 July 2026.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 5
2. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1. Incremental Application of the GREEN Framework . . . . . 5
2.1.1. Use Case Description . . . . . . . . . . . . . . . . 5
2.1.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 7
2.1.3. The Need for Energy Efficiency . . . . . . . . . . . 7
2.1.4. Requirements for GREEN WG . . . . . . . . . . . . . . 7
2.2. Selective reduction of energy consumption in network parts
proportional to traffic levels . . . . . . . . . . . . . 7
2.2.1. Use Case Description . . . . . . . . . . . . . . . . 7
2.2.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 8
2.2.3. The Need for Energy Efficiency . . . . . . . . . . . 8
2.2.4. Requirements for GREEN WG . . . . . . . . . . . . . . 8
2.3. Reporting on Lifecycle Management . . . . . . . . . . . . 8
2.3.1. Use Case Description . . . . . . . . . . . . . . . . 8
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2.3.2. Carbon Reporting . . . . . . . . . . . . . . . . . . 8
2.3.3. Energy Mix . . . . . . . . . . . . . . . . . . . . . 9
2.3.4. GREEN WG Charter Specifics . . . . . . . . . . . . . 9
2.3.5. The Need for Energy Efficiency . . . . . . . . . . . 9
2.3.6. Requirements for GREEN WG . . . . . . . . . . . . . . 9
2.4. Real-time Energy Metering of Virtualised or Cloud-native
Network Functions . . . . . . . . . . . . . . . . . . . 9
2.4.1. Use Case Description . . . . . . . . . . . . . . . . 9
2.4.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 9
2.4.3. The Need for Energy Efficiency . . . . . . . . . . . 10
2.4.4. Requirements for GREEN WG . . . . . . . . . . . . . . 10
2.5. Indirect Energy Monitoring and control . . . . . . . . . 10
2.5.1. Use Case Description . . . . . . . . . . . . . . . . 10
2.5.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 10
2.5.3. The Need for Energy Efficiency . . . . . . . . . . . 10
2.5.4. Requirements for GREEN WG . . . . . . . . . . . . . . 11
2.6. Consideration of other domains for obtention of end-to-end
metrics . . . . . . . . . . . . . . . . . . . . . . . . 11
2.6.1. Use Case Description . . . . . . . . . . . . . . . . 11
2.6.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 11
2.6.3. The Need for Energy Efficiency . . . . . . . . . . . 11
2.6.4. Requirements for GREEN WG . . . . . . . . . . . . . . 12
2.7. Dynamic adjustment of network element throughput according
to traffic levels in wireless transport networks . . . . 12
2.7.1. Use Case Description . . . . . . . . . . . . . . . . 12
2.7.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 12
2.7.3. The Need for Energy Efficiency . . . . . . . . . . . 12
2.7.4. Requirements for GREEN WG . . . . . . . . . . . . . . 13
2.8. Video streaming use case . . . . . . . . . . . . . . . . 13
2.8.1. Use Case Description . . . . . . . . . . . . . . . . 13
2.8.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 14
2.8.3. The Need for Energy Efficiency . . . . . . . . . . . 14
2.8.4. Requirements for GREEN WG . . . . . . . . . . . . . . 14
2.9. WLAN Network Energy Saving . . . . . . . . . . . . . . . 14
2.9.1. Use Case Description . . . . . . . . . . . . . . . . 14
2.9.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 16
2.9.3. The Need for Energy Efficiency . . . . . . . . . . . 16
2.9.4. Requirements for GREEN WG . . . . . . . . . . . . . . 16
2.9.5. The Need for Energy Efficiency . . . . . . . . . . . 17
2.9.6. Requirements for GREEN WG . . . . . . . . . . . . . . 17
2.10. Fixed Network Energy Saving . . . . . . . . . . . . . . . 17
2.10.1. Use Case Description . . . . . . . . . . . . . . . . 17
2.10.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 18
2.10.3. The Need for Energy Efficiency . . . . . . . . . . . 18
2.10.4. Requirements for GREEN WG . . . . . . . . . . . . . 18
2.11. Energy Efficiency Network Management . . . . . . . . . . 19
2.11.1. Use Case Description . . . . . . . . . . . . . . . . 19
2.11.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 19
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2.11.3. The Need for Energy Efficiency . . . . . . . . . . . 19
2.11.4. Requirements for GREEN WG . . . . . . . . . . . . . 19
2.12. ISAC-enabled Energy-Aware Smart City Traffic
Management . . . . . . . . . . . . . . . . . . . . . . . 20
2.12.1. Use case description . . . . . . . . . . . . . . . . 20
2.12.2. GREEN WG Specifics . . . . . . . . . . . . . . . . . 20
2.12.3. Requirements for GREEN WG . . . . . . . . . . . . . 21
2.13. Double Accounting Open issue . . . . . . . . . . . . . . 22
2.13.1. Use case description . . . . . . . . . . . . . . . . 22
2.13.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 22
2.13.3. The Need for Energy Efficiency . . . . . . . . . . . 22
2.13.4. Requirements for GREEN WG . . . . . . . . . . . . . 22
2.14. Energy Efficiency Under Power Shortage . . . . . . . . . 22
2.14.1. Use case description . . . . . . . . . . . . . . . . 23
2.14.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 23
2.14.3. The Need for Energy Efficiency . . . . . . . . . . . 23
2.14.4. Requirements for GREEN WG . . . . . . . . . . . . . 24
2.15. Energy-Efficient Management of Distributed AI Training
Workloads . . . . . . . . . . . . . . . . . . . . . . . 24
2.15.1. Use Case Description . . . . . . . . . . . . . . . . 24
2.15.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 24
2.15.3. The Need for Energy Efficiency . . . . . . . . . . . 24
2.15.4. Requirements for GREEN WG . . . . . . . . . . . . . 24
2.16. Network-level Cross Layer Energy Saving . . . . . . . . . 25
2.16.1. Use Case Description . . . . . . . . . . . . . . . . 25
2.16.2. GREEN WG Charter Specifics . . . . . . . . . . . . . 25
2.16.3. The Need for Energy Efficiency . . . . . . . . . . . 26
2.16.4. Requirements for GREEN WG . . . . . . . . . . . . . 26
3. Security Considerations . . . . . . . . . . . . . . . . . . . 26
4. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 26
5. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 27
6. Use Cases Living List . . . . . . . . . . . . . . . . . . . . 27
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 27
7.1. Normative References . . . . . . . . . . . . . . . . . . 27
7.2. Informative References . . . . . . . . . . . . . . . . . 27
8. Appendix I: Template preparation . . . . . . . . . . . . . . 28
8.1. Use Case Description . . . . . . . . . . . . . . . . . . 28
8.2. GREEN WG Charter Specifics . . . . . . . . . . . . . . . 28
8.2.1. The Need for Energy Efficiency . . . . . . . . . . . 28
8.3. Requirements for GREEN . . . . . . . . . . . . . . . . . 28
9. Appendix II: Necessity and Impact of a Framework for Energy
Efficiency Management . . . . . . . . . . . . . . . . . . 30
9.1. Framework Necessity . . . . . . . . . . . . . . . . . . . 30
9.2. Use Cases Calling for a Framework . . . . . . . . . . . . 31
9.3. Impact on Energy Metrics . . . . . . . . . . . . . . . . 31
9.4. Current Device Readiness . . . . . . . . . . . . . . . . 32
9.5. Why Now? . . . . . . . . . . . . . . . . . . . . . . . . 32
10. Informative References . . . . . . . . . . . . . . . . . . . 33
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Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 34
1. Introduction
This document groups use cases collected from operators and from
discussions since the GREEN WG preparations.
It provides a set of use cases for Energy efficiency Management of
network devices. The scope is devices like switches, routers,
servers and storage devices having an IP address providing a
management interface. It includes their built-in components that
receive and provide electrical energy.
In annex we recall the framework where the use cases can be put in
situation.
2. Use Cases
This section describes a number of relevant use cases with the
purpose of elicit requirements for Energy Efficiency Management.
This is a work in progress and additional use cases will be
documented in next versions of this document. Use cases which are
not tied enough to the current GREEN chater will be moved to the
GREEN WG wiki pages or to other WGs or RGs.
2.1. Incremental Application of the GREEN Framework
2.1.1. Use Case Description
This section describes an incremental example [legacy-path] of usage
showing how a product, a service and a network can use the framework
in different settings.
This use case is the less trendy of all the use cases by far as its
ambitious is limited to migration and coexistence, as usual.
Nevertheless from a telco perspective, it is the centrality for 2
main reasons:
* to start immediatly the move to energy efficiency using legacy
devices;
* to account the gain of energy efficiency during incremental
deployment of energy efficient network components;
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Legacy routers, equipped with traditional Ethernet ports and optical
interfaces will continue to operate within the network. As part of
broader sustainability and energy efficiency goals, there is interest
in exploring the incremental integration of such devices into energy
efficiency framework deployments.
Two directions are considered:
* Improving energy efficiency of legacy devices through targeted
upgrades such as replacing line cards, optimizing firmware
behavior, or reconfiguring interface usage based on operational
demand.
* Including legacy devices in early phases of energy-aware system
deployment, ensuring that improvements are not limited only to new
hardware generations.
Legacy devices can still contribute to reducing overall power
consumption and lowering resource usage and associated environmental
impact. Supporting these incremental improvements helps bridge the
gap between existing infrastructure and modern energy-aware network
strategies.
Device moving gradually to GREEN energy efficiency supports:
* step 1 "baseline" : establishing a reference point of typical
energy usage, which is crucial for identifying inefficiencies and
measuring improvements over time. At this step the controler use
only the (c) part of the framework. It is collected from the
datasheet.
By establishing a baseline and using benchmarking, you can determine
if your networking equipment is performing normally or if it is "off"
from expected performance, guiding you in making necessary
improvements.
The initial measurement of your networking equipment's energy
efficiency and performance, aka Baselining, needs to be in
coordination with the vendor specifications and industry standards to
understand what is considered normal or optimal performance. example:
Baseline: Your switches operate at 5 Gbps per watt. Benchmarking:
Vendor specification is 8 Gbps per watt; industry standard is 10 Gbps
per watt. Action: Implement energy-saving measures and upgrades.
Tracking: Measure again to see if efficiency improves towards 8-10
Gbps per watt.
* step 2 "component": part of the device hw or sw migrated to
support GREEN framework elements.
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* step 3 "device controleur"
* step 4 "network level"
2.1.2. GREEN WG Charter Specifics
This use case demonstrates how Energy Efficiency can be incrementally
applied and measured in legacy networks.
2.1.3. The Need for Energy Efficiency
Ensures that energy efficiency can be deployed, operated and measured
per components, without waiting for full infrastructure upgrades.
2.1.4. Requirements for GREEN WG
* Baseline Measurement: Ability to establish reference energy usage
per device (from datasheets or monitoring).
* Component-Level Upgradability: Support partial migration of device
subsystems to GREEN-aware models.
* Legacy Compatibility: Ensure the framework can include legacy
equipment alongside GREEN-enabled devices.
* Energy Saving Validation: Mechanisms to measure and verify actual
energy savings over time.
* Protection from Overuse: Avoid frequent power cycling that may
damage sensitive components like lasers or connectors.
2.2. Selective reduction of energy consumption in network parts
proportional to traffic levels
2.2.1. Use Case Description
Traffic levels in a network follow patterns reflecting the behavior
of consumers. Those patterns show periodicity in the terms of the
traffic delivered, that can range from daily (from 00:00 to 23:59) to
seasonal (e.g., winter to summer), showing peaks and valleys that
could be exploited to reduce the consumption of energy in the network
proportionally, in case the underlying network elements incorporate
such capabilities. The reduction of energy consumption could be
performed by leveraging on sleep modes in components up to more
extreme actions such as switching off network components or modules.
Such decisions are expected to no impact on the service delivered to
customers, and could be accompanied by traffic relocation and / or
concentration in the network.
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2.2.2. GREEN WG Charter Specifics
This use case fits within the GREEN WG's objectives by emphasizing
energy-aware operational adjustments across network infrastructure
that optimize energy use based on traffic loads and the intelligent
activation/deactivation of resources.
2.2.3. The Need for Energy Efficiency
Reducing energy usage during during low-demand periods can lower
operational costs and carbon emissions while also prolonging
equipment lifespan.
2.2.4. Requirements for GREEN WG
* Support for device and component-level sleep, standby, and
hibernation modes.
* Component-level control (e.g., ports, modules).
2.3. Reporting on Lifecycle Management
2.3.1. Use Case Description
Lifecycle information related to manufacturing energy costs,
transport, recyclability, and end-of-life disposal impacts is part of
what is called "embedded carbon." This information is considered to
be an estimated value, which might not be implemented today in the
network devices. It might be part of the vendor information, and to
be collected from datasheets or databases. In accordance with ISO
14040/44, this information should be considered as part of the
sustainable strategy related to energy efficiency. Also, refer to
the ecodesign framework [(EU) 2024/1781] published in June by the
European Commission.
2.3.2. Carbon Reporting
To report on carbon equivalents for global reporting, it is important
to correlate the location where the specific entity/network element
is operating with the corresponding carbon factor. Refer to the
world emission factor from the International Energy Agency (IEA),
electricity maps applications that reflect the carbon intensity of
the electricity consumed, etc.
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2.3.3. Energy Mix
Energy efficiency is not limited to reducing the energy consumption,
it is common to include carbon free, solar energy, wind energy,
cogeneration in the efficiency.
The type of the sources of energy of the power is one criteria of
efficiency.
There are other dimensions that must visible: As many telecom
locations include battery or additionnally several backups levels (as
example battery, standby generator ...) there is a requirement to
known exactly when a backup power is in used and which one is.
2.3.4. GREEN WG Charter Specifics
Capture lifecycle energy data and integrate it with operational
metrics.
2.3.5. The Need for Energy Efficiency
Considering energy from production to disposal supports the broader
goal of reducing total environmental impact.
2.3.6. Requirements for GREEN WG
* Awareness of backup systems (e.g., batteries, generators).
* Data ingestion from vendor databases or datasheets.
2.4. Real-time Energy Metering of Virtualised or Cloud-native Network
Functions
2.4.1. Use Case Description
Cloud-native and virtualized functions require precise real-time
energy measurements to manage their dynamic workloads and
infrastructure efficiently. Effective metering of virtualized
network infrastructure is critical for the efficient management and
operation of next-generation mobile networks [GREEN_NGNM].
2.4.2. GREEN WG Charter Specifics
Meter and manage energy at both hardware and software layers.
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2.4.3. The Need for Energy Efficiency
Granular and real-time insights into energy use enable optimization
of virtualized workloads, leading to reduced energy footprints.
2.4.4. Requirements for GREEN WG
// TODO.
2.5. Indirect Energy Monitoring and control
2.5.1. Use Case Description
There are cases where Energy Management for some devices need to
report on other entities. There are two major reasons for this.
o For monitoring energy consumption of a particular entity, it is not
always sufficient to communicate only with that entity. When the
entity has no instrumentation for determining power, it might still
be possible to obtain power values for the entity via communication
with other entities in its power distribution tree. A simple example
of this would be the retrieval of power values from a power meter at
the power line into the entity. A Power Distribution Unit (PDU) and
a Power over Ethernet (PoE) switch are common examples. Both supply
power to other entities at sockets or ports, respectively, and are
often instrumented to measure power per socket or port. Also it
could be considered to obtain power values for the entity via
communication with other entities outside of the power distribution
tree, like for example external databases or even data sheets.
o Similar considerations apply to controlling the power supply of an
entity that often needs direct or indirect communications with
another entity upstream in the power distribution tree. Again, a PDU
and a PoE switch are common examples, if they have the capability to
switch power on or off at their sockets or ports, respectively.
2.5.2. GREEN WG Charter Specifics
inclusion of legacy or non-instrumented devices.
2.5.3. The Need for Energy Efficiency
Energy monitoring across the network, even for devices that lack
built-in sensors.
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2.5.4. Requirements for GREEN WG
* Indirect control mechanisms.
* Integration with external databases or datasheets.
2.6. Consideration of other domains for obtention of end-to-end metrics
2.6.1. Use Case Description
The technologies under the scope of IETF provide the necessary
connectivity to other technological domains. For the obtention of
metrics end-to-end it would be required to combine or compose the
metrics per each of those domains.
An exemplary case is the one of a network slice service. The concept
of network slice was initially defined by 3GPP [TS23.501], and it has
been further extended to the concerns of IETF [RFC9543].
In regards energy efficiency, 3GPP defines a number of energy-related
key performance indicators (KPI) in [TS28.554], specifically Energy
Efficiency (EE) and Energy Consumption (EC) KPIs. There are KPIs
particular for a slice supporting a specific kind of service (e.g.,
Mobile Broadband or MBB), or generic ones, like Generic Network Slice
EE or Network Slice EC. Assuming these as the KPIs of interest, the
motivation of this use case is the obtention of the equivalent KPIs
at IETF level, that is, for the network slice service as defined in
[RFC9543].
Note that according to [TS28.554], the Generic Network Slice EE is
the performance of the network slice divided by the Network Slice EC.
Same approach can be followed at IETF level. Note that for avoiding
double counting the energy at IETF level in the calculation of the
end-to-end metric, the 3GPP metric should only consider the
efficiency and consumption of the 3GPP-related technologies.
2.6.2. GREEN WG Charter Specifics
cross-domain measurement alignment.
2.6.3. The Need for Energy Efficiency
Cross-domain energy visibility is essential for services spanning
multiple infrastructure providers and technologies.
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2.6.4. Requirements for GREEN WG
* Avoidance of double accounting.
* Metric mapping and transformation.
2.7. Dynamic adjustment of network element throughput according to
traffic levels in wireless transport networks
2.7.1. Use Case Description
Radio base stations are typically connected to the backbone network
by means of fiber or wireless transport (e.g., microwave)
technologies. In the specific case of wireless transport, automation
frameworks have been defined [ONF-MW][RFC8432][mWT025] for their
control and management.
One of the parameters subject of automated control is the power of
the radio links. The relevance of that capability is that the power
can be adjusted accordingly to the traffic observed. Wireless
transport networks are typically planned to support the maximum
traffic capacity in their area of aggregation, that is, the traffic
peak. With that input, the number of radio links in the network
element and the corresponding power per radio link (for supporting a
given modulation and link length in the worst weather conditions) are
configured. This is done to avoid any kind of traffic loss in the
worst operational situation. However, such operational needs are
sporadic, giving room for optimization during normal operational
circumstances and/or low traffic periods.
Power-related parameters are for instance defined in [RFC8561].
Those power parameters can be dynamically configured to adjust the
power to the observed traffic levels with some coarse granularity,
but pursuing certain degrees of proportionality.
2.7.2. GREEN WG Charter Specifics
This aligns with the GREEN WG goals of enabling dynamic and context-
aware energy optimization at the transport layer.
2.7.3. The Need for Energy Efficiency
Wireless links configured for peak traffic are often underutilized,
wasting energy. Adjusting power to match demand can substantially
reduce consumption.
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2.7.4. Requirements for GREEN WG
* Adapt energy consumption to traffic change.
* Dynamic energy efficiency control and optimization.
2.8. Video streaming use case
2.8.1. Use Case Description
Video streaming is nowadays the major source of traffic observed in
ISP networks, in a propotion of 70% or even higher. Over-the-top
distribution of streaming traffic is typically done by delivering a
unicast flow per end user for the content of its interest.In
consequence, during the hours of higher demand, the total traffic in
the network is proportional to the concurrence of users consuming the
video streaming service. The amount of traffic is also dependent of
the resolution of the encoded video (the higher the resolution, the
higher the bit rate per video flow), which tends to be higher as long
as the users devices support such higher resolutions.
The consequence of both the growth in the number of flows to be
supported simultaneously, and the higher bit rate per flow, is that
the nework elements in the path between the source of the video and
the user have to be dimensioned accordingly. This implies the
continuous upgrade of those network elements in terms of capacity,
with the need of deploying high-capacity network elements and
components. Apart from the fact that this process is shortening the
lifetime of network elements, the need of high capacity interfaces
also increase the energy consumption (despite the effort of
manufacturers in creating more efficient network element platforms).
Note that nowadays there is no actual possibility of activating
energy consumption proportionality (in regards the delivered traffic)
to such network elements.
As a mean of slowing down this cycle of continuos renewal, and reduce
the need og higher bit rate interfaces / line cards, it seems
convenient to explore mechanisms that could reduce the volume of
traffic without impacting the user service expectations. Variants of
multicast or different service delivery strategies can help to
improve the energy efficiency associated to the video streaming
service. It should be noted that another front for optimization is
the one related to the deployment of cache servers in the network.
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2.8.2. GREEN WG Charter Specifics
Video streaming represents a large portion of network traffic.
Multicast techniques, adaptive streaming, and strategic caching can
reduce traffic duplication and improve energy efficiency.
2.8.3. The Need for Energy Efficiency
Reducing redundant unicast traffic and improving caching strategies
reduces backbone and access network energy consumption.
2.8.4. Requirements for GREEN WG
* Support for multicast-aware energy metrics.
* Cache server placement optimization.
2.9. WLAN Network Energy Saving
2.9.1. Use Case Description
In a WLAN network, Access Points(APs) are typically powered by Power
over Ethernet (PoE) switches and represent a substantial portion of
the energy consumed by edge network devices due to their high density
and round-the-clock operation.
This use case introduces a multi-mode approach for AP energy saving:
The working status of the AP can be break down into 3 modes as
follows: PoE power-off mode: In this mode, the PoE switch shuts down
the port and stops supplying power to the AP. The AP does not
consume power at all. When the AP wakes up, the port provides power
again. In this mode, it usually takes a few minutes for the AP to
recover. Hibernation mode: Only low power consumption is used to
protect key hardware such as the CPU, and other components are shut
down. Low power consumption mode: Compared with the hibernation
mode, the low power consumption mode maintains a certain
communication capability. For example, the AP retains only the 2.4
GHz band and disables other radio bands.
* PoE Power-Off Mode: The PoE switch disables the port, completely
cutting power to the AP. No energy is consumed, though recovery
takes several minutes when power is restored.
* Hibernation Mode: The AP powers down most components, preserving
minimal CPU functionality to allow faster reactivation.
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* Low Power Mode: The AP disables some radios (e.g., 5GHz),
retaining minimal operation (e.g., 2.4GHz) for reduced but
persistent service.
To maintain coverage and service quality, surrounding APs dynamically
adjust their transmit power when some APs enter energy-saving states.
Energy-saving schedules may be time-based (e.g., during off-hours) or
traffic-aware (low utilization periods).
Grouping APs by location enables coordinated energy-saving plans,
minimizing disruption while maximizing cumulative energy reduction.
/---\
| +-----+
| AP | |
\---/ | +------------+
| | |
|------+ PoE |
/---\ | | Switch |
| | | +------------+
| AP +-----+
\---/
Figure 1: PoE Power Off Mode
4 4
+----------+ \|/ +----------+ \|/
| | | | | |
| +----+ | | | +----+ | |
| |5GHz+-+----+ | |5GHz+-+-X--+
| | RF | | 2 | | RF | | 2
| +----+ | \|/ \ | +----+ | \|/
| +----+ | | ---\ | +----+ | |
| 2.4GHz| | | \ | 2.4GHz| | |
| | RF +-+----+ / | | RF +-+-X--+
| +----+ | 2 ---/ | +----+ | 2
| +----+ | \|/ / | +----+ | \|/
| 2.4GHz| | | | 2.4GHz| | |
| | RF |-+----+ | | RF +-+----+
| +----+ | | +----+ |
+----------+ +----------+
Figure 2: Low Power Consumption Mode
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+--+ +--+ +--+
|AP|--|AP|--- |AP| ------------------------------
+--+ +--+ \+--+ Grouping Recommended
/ \ Area Energy Saving Period
+--+ +--+ +--+ ------------------------------
|AP| |AP| |AP| XED01-1 01:00:00,06:30:00
+--+ +--+ +--+
| | ------------------------------
+--+ +--+
|AP| +--+ /|AP| XED01-2 01:30:00,06:30:00
+--+--|AP|--- +--+ --------------------------------
+--+
Figure 3: Wireless Resource Management on APs
2.9.2. GREEN WG Charter Specifics
This use case aligns with the GREEN WG's charter by:
* Illustrating real-world scenarios where energy efficiency
mechanisms (discovery, monitoring, control) apply to IP-managed
devices.
* Providing a localized but scalable use case that fits into broader
energy-aware network management frameworks.
-Addressing interoperability and observability across energy states
and reporting mechanisms, including energy mix awareness.
2.9.3. The Need for Energy Efficiency
Given the number of deployed APs in enterprise and campus networks,
their continuous operation contributes significantly to energy
consumption. Many of these environments experience well-defined
periods of inactivity (e.g., nighttime, weekends), during which full
AP operation is unnecessary.
Reducing AP energy consumption during these periods, while
maintaining sufficient coverage and quality of service, presents an
effective opportunity for energy savings. Applying coordinated
power-state transitions across AP groups enables measurable
improvements with minimal operational impact.
2.9.4. Requirements for GREEN WG
To support WLAN Network Energy Saving, the GREEN WG should consider:
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* Defining power state transition models and standard energy mode
nomenclature for APs (e.g., OFF, HIBERNATE, LOW-POWER, ACTIVE).
* Specifying APIs or YANG models for monitoring and controlling AP
power modes via PoE switches or WLAN controllers.
* Enabling reporting of per-mode energy consumption, transitions
over time, and cumulative energy savings.
* Ensuring support for scheduled and dynamic (traffic-aware) control
policies.
* Allowing integration with broader network monitoring frameworks
for energy efficiency analysis at the local and network-wide
level.
* Considering implications for resiliency, coverage trade-offs, and
restart delays in power-off scenarios.
Enable measuring and reporting of energy usage through metrics and
attributes and allow operators to optimize energy usage.
2.9.5. The Need for Energy Efficiency
AP nodes as network devices with the largest number consume large
amount of energy.
2.9.6. Requirements for GREEN WG
* Energy saving mode switching based on network condition changes
* Allow network devices shutdown to save energy
* Allow working network devices transmit more power to increase the
coverage of the entire area
2.10. Fixed Network Energy Saving
2.10.1. Use Case Description
In many fixed networks, particularly those at metro or backbone
level, traffic patterns follow a predictable tidal cycle - with
clearly defined high-traffic and low-traffic periods. These
fluctuations provide opportunities for dynamic energy-saving
mechanisms. During low-traffic periods, certain network components
can be deactivated or put into sleep mode. Additionally, routers
equipped with interfaces of varying speeds (e.g., from 1G to 400G)
can dynamically adjust interface speeds, deactivate unused ports, or
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scale down internal resources such as processor cores, chipset clock
frequencies, or SerDes lanes, depending on traffic demand.
In many fixed networks, particularly those at metro or backbone
level, traffic patterns may follow a predictable tidal cycle - with
clearly defined high-traffic and low-traffic periods, or may follow
unbalanced traffic load - with clearly identified high-load and idle
network elements. These fluctuations provide opportunities for
dynamic energy-saving mechanisms. During low-traffic periods or
unbalanced traffic load periods, the low traffic could be migrated
and aggregated, certain network components can be deactivated or put
into sleep mode. Additionally, routers equipped with interfaces of
varying speeds (e.g., from 1G to 400G) can periods dynamically adjust
interface speeds, deactivate unused ports, or scale down internal
resources such as processor cores, chipset clock frequencies, or
SerDes lanes, depending on traffic demand.
2.10.2. GREEN WG Charter Specifics
The GREEN working group can contribute by defining standard
mechanisms and protocols to: - Monitor traffic load in a standardized
and interoperable manner. - Communicate energy-saving intents across
network elements (e.g., turning off links or reducing interface
speeds). - Signal state transitions (e.g., from active to low-power
states) reliably, taking into account the need for fast reactivation
during traffic bursts. - Ensure compatibility with QoS and network
availability requirements.
2.10.3. The Need for Energy Efficiency
Network devices at metro or backbone network consume large amount of
energy.
2.10.4. Requirements for GREEN WG
* Standardized definitions and telemetry models for identifying
tidal traffic patterns and low-utilization windows.
* Standardized AI/ML models for traffic prediction using historical
data, capturing both long-term regularities and short-term bursts.
* Protocol support for energy-aware dynamic reconfiguration (e.g.,
speed adjustment, core deactivation).
* Trade-offs between energy savings and network latency/performance.
* Mechanisms to synchronize energy-saving decisions across multiple
devices (e.g., coordinated interface downshifts).
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* Fail-safe or fallback procedures to ensure robustness in case of
unexpected traffic surges.
2.11. Energy Efficiency Network Management
2.11.1. Use Case Description
Modern network operators need comprehensive visibility into the
energy consumption and efficiency of their infrastructure. This
includes real-time and historical statistics of power usage per
device, identification of devices participating in energy-saving
modes, differentiation between energy-optimized and legacy devices,
and aggregated views of energy trends across the entire network.
Such visibility enables more informed decisions about network
adjustments and optimizations that align with energy efficiency
goals.
2.11.2. GREEN WG Charter Specifics
The GREEN WG has a role in developing interoperable models and
mechanisms for: - Real-time telemetry and historical analysis of
energy metrics. - Mapping energy efficiency indicators to network
topology and traffic load. - Identifying energy-saving capabilities
of devices (e.g., support for interface power scaling, sleep modes).
- Integration with existing network management and orchestration
systems. - Encouraging adoption of GREEN-compliant energy
observability in vendor equipment.
2.11.3. The Need for Energy Efficiency
// TODO.
2.11.4. Requirements for GREEN WG
* Standardized YANG models or data formats for energy metrics and
efficiency reporting.
* Methods to correlate energy usage with traffic load and service
demands.
* Interfaces for exposing energy capabilities and statuses of
devices in a vendor-neutral way.
* Security and privacy implications of exposing energy-related
telemetry.
* Guidelines for presenting energy insights to operators in a way
that supports actionable decisions.
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2.12. ISAC-enabled Energy-Aware Smart City Traffic Management
2.12.1. Use case description
Integrated Sensing and Communications (ISAC) is emerging as a key
enabler for next-generation wireless networks, integrating sensing
and communication functionalities within a unified system. By
leveraging the same spectral, hardware, and computational resources,
ISAC enhances network efficiency while enabling new capabilities such
as high-resolution environment perception, object detection, and
situational awareness. This paradigm shift is particularly relevant
for applications requiring both reliable connectivity and precise
sensing, such as autonomous vehicles, industrial automation, and
smart city deployments. Given its strategic importance, ISAC has
gained significant traction in standardization efforts. The ETSI
Industry Specification Group (ISG) on ISAC has been established to
explore technical requirements and use cases, while 3GPP has
initiated discussions on ISAC-related features within its ongoing
research on future 6G systems. Furthermore, research initiatives
within the IEEE and IETF are investigating how ISAC can be integrated
into network architectures, spectrum management, and protocol design,
making it a critical area of development in the evolution of wireless
networks.
This use case involves deploying ISAC systems in a smart city to
monitor and optimize vehicles' traffic flows while minimizing energy
consumption of the mobile network. The system integrates sensing
technologies, such as radar and LIDAR, with communication networks to
detect vehicle density, monitor road conditions, and communicate with
autonomous vehicles or traffic lights. By using ISAC, the system
minimizes redundant infrastructure (e.g., separate sensors and
communication equipment), thus reducing the overall carbon and energy
footprint.
On the other hand, such an infrastructure will have to adapt its
energy optimization policies to sensing applications: critical
functions (e.g., threat detection) must run continuously, while
others should activate depending on the context.
2.12.2. GREEN WG Specifics
Energy Consumption Monitoring: Each ISAC component (e.g., roadside
units, integrated sensors, and communication transceivers) is capable
of reporting its energy consumption in real time to the centralized
or distributed energy management system.
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Reconfiguration for Energy Efficiency: The system can dynamically
switch between high-resolution sensing modes (e.g., during peak
hours) and low-power modes (e.g., during low traffic periods). The
network can reconfigure traffic communication paths to prioritize
routes or nodes that consume less power, leveraging energy-efficient
communication protocols.
Integration of Local and Global Energy Goals: The system can operate
both locally (e.g., turning off specific roadside units in low-
traffic areas) and globally (e.g., modifying traffic patterns across
the city) to achieve defined energy consumption goals.
2.12.3. Requirements for GREEN WG
1. Measurement Granularity:
* Ability to measure energy consumption per ISAC component (e.g.,
roadside unit, sensor, transceiver).
* Granular reporting per communication link or sensing mode (e.g.,
high-power radar mode vs. low-power mode).
1. Power Control Mechanisms:
* Ability to switch components on/off or place them in sleep/standby
mode when not in use.
* Support for dynamic adjustment of sensing resolution or
communication bandwidth to balance energy savings and system
performance.
1. Reconfiguration and Adaptability:
* Support for hardware reconfiguration (e.g., adaptive sensing
modes, transceiver settings) to optimize energy use.
* Mechanisms to steer traffic or adjust network routing based on
global or local energy-saving objectives.
1. Global Coordination:
* Capabilities for cross-domain coordination to enable global
optimization (e.g., city-wide traffic rerouting or dynamic
resource allocation across different regions).
* Ability to aggregate and analyze energy consumption data from all
ISAC components to inform high-level decision-making.
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1. Energy-Aware Standards and Protocols:
* Communication protocols that minimize power usage while
maintaining reliability.
* Interoperability standards for energy-aware reconfiguration
across heterogeneous ISAC components and systems.
2.13. Double Accounting Open issue
2.13.1. Use case description
Energy consumption monitoring often includes metering at both
upstream and downstream levels of power distribution. While this can
provide granular visibility, it may also lead to double accounting if
not carefully managed.
A common case arises when energy is measured at the input of a Power
Delivery Unit (PDU), and individually at each device powered by that
PDU (e.g., servers, switches). Since the PDU input already reflects
the downstream consumption, summing the per-device values with the
PDU input results in redundant reporting. A similar issue occurs
with Power over Ethernet (PoE) infrastructures when a network switch
supply power directly to devices. If the total power consumption
measured encompasses both the power delivered to the PoE switch and
to the powered devices, this again results in double accounting.
These 2 cases distort energy dashboards and indicators such as Power
Usage Effectiveness (PUE).
2.13.2. GREEN WG Charter Specifics
Unlike most of the WGs, the GREEN WG purpose sums the constraints of
data networks and grid/off-grid networks, independantly of the
location of the network domain in the architecture (aka edge,
core...): - include the grid network picture in networks operation
2.13.3. The Need for Energy Efficiency
// TODO.
2.13.4. Requirements for GREEN WG
The monitoring must not count twice the power that passthru devices
and components monitored, including legacy elements.
2.14. Energy Efficiency Under Power Shortage
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2.14.1. Use case description
This use case focuses on network devices (e.g., routers, switches,
access points) that must maintain essential connectivity during power
shortages. Telecom locations use different power backups levels (as
example battery, standby generator ...). Devices may have access to
one or more backup power sources such as onboard batteries, PoE
fallback, or centralized UPS systems. When a power shortage occurs,
the network device transitions from grid power to available backup
sources and must prioritize operational resilience over typical
energy optimization strategies. Unlike behavior in a normally
powered state, the focus here is not on minimizing energy consumption
per se, but on sustaining essential operation with limited energy and
prepare to worse situations and more constrained powered state
fallbacks. These behaviors increase the device's ability to operate
longer under backup power, ensuring availability of essential
services during outages.
Data networks and grid networks resiliency are closely interleaved
during power shortage. It is a race between the speed of the
operations to restore the grid network and the availability of mobile
connectivity for power grid repair teams because of the impairment of
operational visibility and response coordination.
Network constraints differ in sparse or dense situations but shortage
impacts change accordingly. This is becoming crucial and not limited
to sparse environments where stable power supply is well known to not
be guaranteed: it applies to dense cities' utilities which operations
are coupled to the simoultaneous availability of both power and
persistent data communication and compute at the edge.
2.14.2. GREEN WG Charter Specifics
Unlike most of the WGs, the GREEN WG purpose sums the constraints of
data networks and grid/off-grid networks, independantly of the
location of the network domain in the architecture (aka edge,
core...): - Improved networks resiliency by making energy constraints
an input into the network's operations.
2.14.3. The Need for Energy Efficiency
Energy efficiency under power shortage conditions is fundamentally
different from routine energy optimization. In this context, energy
is a finite and rapidly depleting resource, not just an environmental
concern or cost factor: - Optimize backup power usage for resilience
- Maintain critical networking capabilities during power shortage
events - Maximize operational uptime using fallback power sources
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2.14.4. Requirements for GREEN WG
* Awareness of backup systems (e.g., batteries, generators).
* Awareness of hierarchical fallback to more constrained powered
state.
2.15. Energy-Efficient Management of Distributed AI Training Workloads
2.15.1. Use Case Description
Training large AI models requires distributed computing across
multiple servers or GPUs. This distributed training generates
significant East-West traffic as data is exchanged between nodes.
This use case focuses on managing the energy consumption of
distributed AI training workloads by optimizing data placement,
communication patterns, and compute resource allocation. Strategies
include scheduling training jobs to run during periods of lower
energy prices, using compression techniques to reduce data transfer
volume, and dynamically adjusting the number of active nodes based on
training progress. It is also critical to have a cross-domain view
of the end to end flow to address power consumption holistically.
2.15.2. GREEN WG Charter Specifics
This use case contributes to the GREEN WG's goals by addressing the
energy efficiency of emerging workloads and exploring the use of
dynamic resource allocation to minimize energy consumption. It calls
for energy-aware scheduling and optimization techniques.
2.15.3. The Need for Energy Efficiency
AI training is a computationally intensive task that consumes a
significant amount of energy. Optimizing the energy efficiency of
distributed AI training workloads can reduce costs, improve
sustainability, and enable more widespread adoption of AI
technologies. There is an impact not only for the network
consumption, rather than the compute consumption.
2.15.4. Requirements for GREEN WG
* East-West Traffic Monitoring: Standardized mechanisms for
monitoring the volume, type, and characteristics of East-West
traffic.
* Workload Characterization: Standardized methods for characterizing
the energy consumption profile of AI training workloads.
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* Energy-Aware Scheduling: APIs for scheduling training jobs based
on energy prices, grid conditions, and other energy-related
factors.
* Data Compression and Optimization: Techniques for reducing the
volume of data transferred during distributed training.
* Dynamic Resource Allocation: Mechanisms for dynamically adjusting
the number of active nodes based on training progress and energy
availability.
* Resource co-location, so the data used for processing can be as
close as possible to the data crunching machines.
2.16. Network-level Cross Layer Energy Saving
2.16.1. Use Case Description
For the carrier of integrated services (e.g., TDM/IP) using physical
infrastructures like optical fiber and optical wavelengths, the
transport network functions as the underlay to the IP network as the
overlay. Here the transport network is designed for guaranteed
connectivity, with relatively rigid resource allocation while the IP
network supports flexible traffic on runtime. the technology
difference creates the challenge of "dynamic traffic vs. static
infrastructure". In the section 2.10, fixed network energy saving
use case describes that during low-traffic periods or unbalanced
traffic load periods, certain network components can be deactivated
or put into sleep mode. Imagine when an IP line card is shutdown, it
not only affects the link in the overlying IP layer (L3) but also the
link in the transport layer (L0/L1/L2), network-level cross-layer
energy saving management can enable a holistic optimization of
resources across layers while ensuring the overall service QoS. This
allows some resources across layers to enter a sleep or lower-power
state, thereby reducing overall energy consumption.
2.16.2. GREEN WG Charter Specifics
* Multi-layer energy management: This use case highlights the
interaction between the transport network underlay (L0-L2) and the
IP overlay (L3). The GREEN WG can define how energy-saving
actions are safely coordinated across layers to maintain service
continuity, which is a critical aspect of cross-layer
optimization.
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2.16.3. The Need for Energy Efficiency
When talking about network-level energy saving, It's inevitable to
face the challenge of "dynamic traffic vs. static infrastructure" and
deal with different network layers.
2.16.4. Requirements for GREEN WG
* Mechanisms to coordinate energy-saving decisions across multiple
layers(e.g., coordination between IP layer and Transport layer).
3. Security Considerations
Energy efficiency management comes with numerous security
considerations :
Controlling Power State and power supply of entities are considered
highly sensitive actions, since they can significantly affect the
operation of directly and indirectly connected devices. Therefore,
all control actions must be sufficiently protected through
authentication, authorization, and integrity protection mechanisms.
Entities that are not sufficiently secure to operate directly on the
public Internet do exist and can be a significant cause of risk, for
example, if the remote control functions can be exercised on those
devices from anywhere on the Internet.
The monitoring of energy-related quantities of an entity as addressed
can be used to derive more information than just the received and
provided energy; therefore, monitored data requires protection. This
protection includes authentication and authorization of entities
requesting access to monitored data as well as confidentiality
protection during transmission of monitored data. Privacy of stored
data in an entity must be taken into account. Monitored data may be
used as input to control, accounting, and other actions, so integrity
of transmitted information and authentication of the origin may be
needed.
4. IANA Considerations
This document has no IANA actions.
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5. Acknowledgments
The contribution of Luis M. Contreras to this document has been
supported by the Smart Networks and Services Joint Undertaking (SNS
JU) under the European Union's Horizon Europe research and innovation
projects 6Green (Grant Agreement no. 101096925) and Exigence (Grant
Agreement no. 101139120).
6. Use Cases Living List
Consider 5g vs network slicing: 3GPP spec describing energy
efficiency KPIs. 3GPP TS 28.554.
Reference:https://datatracker.ietf.org/doc/rfc9543/ Connectivity from
radio side (trying to control the traffic/related work to CCAMP)
Marisol to add one use case: drift from data specifications...
(somehow link to the above) Energy Metric in E2E view
7. References
7.1. Normative References
[IEC.61850-7-4] International Electrotechnical Commission,
"Communication networks and systems for power utility automation --
Part 7-4: Basic communication structure -- Compatible logical node
classes and data object classes", March 2010.
[IEC.62053-21] International Electrotechnical Commission,
"Electricity metering equipment (a.c.) -- Particular requirements --
Part 21: Static meters for active energy (classes 1 and 2)", January
2003.
[IEC.62053-22] International Electrotechnical Commission,
"Electricity metering equipment (a.c.) -- Particular requirements --
Part 22: Static meters for active energy (classes 0,2 S and 0,5 S)",
January 2003.
[ATIS-0600015.03.2013] ATIS, "ATIS-0600015.03.2013: Energy Efficiency
for Telecommunication Equipment: Methodology for Measurement and
Reporting for Router and Ethernet Switch Products", 2013.
7.2. Informative References
[IEC.60050] International Electrotechnical Commission, "Electropedia:
The World's Online Electrotechnical Vocabulary", 2013,
http://www.electropedia.org/iev/iev.nsf/welcome?openform
(http://www.electropedia.org/iev/iev.nsf/welcome?openform).
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8. Appendix I: Template preparation
This appendix should be removed when the template will be stable.
It is based on the example from https://datatracker.ietf.org/doc/
rfc9450/.
8.1. Use Case Description
General description of the use case.
8.2. GREEN WG Charter Specifics
(if there are no GREEN specific aspects, then it is not a UC to be
documented) For example, the use case involves components that can
report on energy consumption and that might be reconfigured (on a
local or global scale) to operate based on energy goals/limitations.
8.2.1. The Need for Energy Efficiency
8.3. Requirements for GREEN
Examples (can be split into different categories to facilitate a
summary at the end of the document):
* Granularity of measurements should be per component, per line, per
port.
* Ability to switch on/off, put on sleep mode' components.
* Ability to reconfigure hardware mode based on power savings (e.g.,
reduce reliability or speed).
* Ability to operate globally (not constrained to just one device)
based on power savings/goals (e.g., steer traffic using a
different path that consumes less energy).
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(a) (b) (c)
Inventory Monitor +- DataSheets/DataBase and/or via API
Of identity Energy | Metadata and other device/component
and Capability Efficiency | /network related information:
^ ^ |
| | | .Power/Energy related metrics
| | | .information
| | | .origin of Energy Mix
| | | .carbon aware based on location
| | |
| | |
| | |
| | v
+--------------------------------------------------------------------+
| * |
| (2) controller (collection, compute and aggregate?) |
| |
+--------------------------------------------------------------------+
^ ^ ^ |
(d) | (e) | (f) | |(g)
Inventory | Monitor | GREEN WG: | |GREEN WG: Control
Capability | Traffic | Monitor power | |(Energy saving
| & power | Proportion, | |Functionality
| consumption | Energy efficiency| |Localized mgmt/
| | ratio, etc) | |network wide mgmt)
| | | |
| | | |
| | | v
+--------------------------------------------------------------------+
| * |
| (1) Device/Component Level |
| |
| +---------+ +-----------+ +----------------+ +----------------+ |
| | (I) | | (II) | | (III) | | (IV) | |
| | Network | | Device | | Legacy Network | | 'Attached'(PoE | |
| | Device | | Component | | Device | | kind) Device | |
| | | | | | | | | |
| +---------+ +-----------+ +----------------+ +----------------+ |
+--------------------------------------------------------------------+
(*) Energy Efficiency Management Function is implemented inside the
device or in a controller
Figure 4: Framework discussed during the BoF
The main elements in the framework are as follows:
(a),(d) Discovery and Inventory
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(b),(c) GREEN Metrics
(b),(f) Monitor energy efficiency
(e) Monitor power consumption and traffic (IPPM WG throughput,
traffic load, etc)
(g) Control Energy Saving
9. Appendix II: Necessity and Impact of a Framework for Energy
Efficiency Management
This appendix outlines the necessity of defining a framework for
energy efficiency management within the GREEN Working Group's current
phase. Establishing a framework now is crucial for standardizing
processes, optimizing energy usage, and ensuring interoperability
across network devices. Immediate action enables the industry to
achieve cost savings, meet regulatory requirements, and maintain
competitiveness. By utilizing insights from existing use cases, the
framework can deliver actionable metrics and support ongoing
innovation, positioning the industry to effectively manage future
energy challenges.
9.1. Framework Necessity
Analyzing use cases such as the "Incremental Application of the GREEN
Framework" reveals the critical need for a structured approach to
transitioning network devices towards energy-efficient operations.
The framework is essential for:
* *Standardization*: Ensuring consistent practices across different
devices and network segments to facilitate interoperability.
* *Efficient Energy Management*: Providing guidelines to identify
inefficiencies and implement improvements.
* *Scalability*: Offering solutions that accommodate growing network
demands and complexity.
* *Cost Reduction*: Optimizing energy usage to lower operational
costs and extend equipment lifecycles.
* *Competitiveness*: Enabling organizations to maintain a
competitive edge through enhanced sustainability.
* *Environmental Impact*: Supporting broader sustainability
initiatives by reducing carbon footprints.
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* *Simplified Implementation*: Streamlining the deployment of
energy-efficient measures to minimize service disruptions.
* *Security*: Protecting sensitive operations related to power
states and consumption.
9.2. Use Cases Calling for a Framework
Multiple use cases underscore the need for a framework, including:
* *Incremental Application of the GREEN Framework*
* *Selective Reduction of Energy Consumption in Network Parts*
* *Real-time Energy Metering of Virtualized or Cloud-native Network
Functions*
* *Indirect Energy Monitoring and Control*
* *Consideration of Other Domains for Obtention of End-to-End
Metrics*
* *Dynamic Adjustment of Network Element Throughput*
* *Video Streaming Use Case*
* *WLAN Network Energy Saving*
* *Fixed Network Energy Saving*
* *Energy Efficiency Network Management*
These use cases highlight diverse aspects of energy management that
require a cohesive framework for effective implementation.
9.3. Impact on Energy Metrics
The framework will significantly enhance the creation of energy
metrics with actionable insights by:
* *Standardizing Metrics*: Establishing consistent measurement
protocols for energy consumption and efficiency.
* *Enhancing Data Collection*: Facilitating comprehensive monitoring
and data aggregation across devices.
* *Supporting Real-time Monitoring*: Enabling dynamic tracking and
immediate optimization of energy usage.
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* *Integration Across Devices*: Ensuring interoperability for
network-wide data analysis.
* *Providing Actionable Insights*: Translating raw data into
meaningful information for decision-making.
9.4. Current Device Readiness
While many modern networking devices have basic energy monitoring
capabilities, these are often proprietary. The framework will define
requirements to enhance these capabilities, enabling standardized
metric production and meaningful data contributions for energy
management goals.
9.5. Why Now?
The decision to define the framework now, rather than later, is
driven by:
* *Immediate Benefits*: Start realizing cost savings, reduced carbon
footprints, and improved efficiencies.
* *Rapid Technological Advancements*: Aligning the framework with
current technologies to prevent obsolescence.
* *Increasing Energy Demands*: Mitigating the impact of growing
energy consumption on costs and sustainability.
* *Regulatory Pressure*: Preparing for compliance with existing and
anticipated sustainability regulations.
* *Competitive Advantage*: Positioning organizations as leaders in
sustainability and innovation.
* *Foundational Work Ready*: Building on the use cases and
requirements established in Phase I.
* *Proactive Risk Management*: Minimizing risks associated with
energy costs and environmental factors.
* *Facilitate Future Innovations*: Creating a platform for
continuous improvements and adaptations.
* *Stakeholder Engagement*: Ensuring diverse perspectives are
reflected for broader adoption.
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In conclusion, establishing the framework for energy efficiency
management now is strategic and timely, leveraging the current
momentum of use cases and requirements to drive meaningful progress
in energy efficiency management. Delaying its development could
result in missed opportunities for immediate benefits, increased
costs, and challenges in adapting to future technological and
regulatory landscapes.
10. Informative References
[GREEN_NGNM]
"NGMN Alliance, GREEN FUTURE NETWORKS: METERING IN
VIRTUALISED RAN INFRASTRUCTURE", n.d.,
<https://www.ngmn.org/publications/metering-in-
virtualised-ran-infrastructure.html>.
[legacy-path]
"Requirements for Energy Efficiency Management", 21 July
2024, <https://datatracker.ietf.org/doc/draft-stephan-
legacy-path-eco-design>.
[mWT025] "ETSI GR mWT 025, Wireless Backhaul Network and Services
Automation: SDN SBI YANG models, V1.1.1.", 31 March 2021.
[ONF-MW] "ONF TR-532, Microwave Information Model, version 2.0.",
31 January 2024.
[RFC8432] Ahlberg, J., Ed., Ye, M., Ed., Li, X., Contreras, LM., and
CJ. Bernardos, "A Framework for Management and Control of
Microwave and Millimeter Wave Interface Parameters",
RFC 8432, DOI 10.17487/RFC8432, October 2018,
<https://www.rfc-editor.org/rfc/rfc8432>.
[RFC8561] Ahlberg, J., Ye, M., Li, X., Spreafico, D., and M.
Vaupotic, "A YANG Data Model for Microwave Radio Link",
RFC 8561, DOI 10.17487/RFC8561, June 2019,
<https://www.rfc-editor.org/rfc/rfc8561>.
[RFC9543] Farrel, A., Ed., Drake, J., Ed., Rokui, R., Homma, S.,
Makhijani, K., Contreras, L., and J. Tantsura, "A
Framework for Network Slices in Networks Built from IETF
Technologies", RFC 9543, DOI 10.17487/RFC9543, March 2024,
<https://www.rfc-editor.org/rfc/rfc9543>.
[TS23.501] "3GPP TS 23.501, System architecture for the 5G System
(5GS), 17.6.0.", 22 September 2022.
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[TS28.554] "3GPP TS 28.554, Management and orchestration; 5G end to
end Key Performance Indicators (KPI), 17.15.0.", 25
September 2024.
Authors' Addresses
Emile Stephan
Orange
Email: [email protected]
Marisol Palmero
Individual
Email: [email protected]
Benoit Claise
Huawei
Email: [email protected]
Qin Wu
Huawei
Email: [email protected]
Luis M. Contreras
Telefonica
Email: [email protected]
Carlos J. Bernardos
Universidad Carlos III de Madrid
Email: [email protected]
Xinyu Chen
China Mobile
Email: [email protected]
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