wdiff rfc9845v1.txt rfc9845.txt

Internet Research Task Force (IRTF) A. Clemm, Ed.
Request for Comments: 9845 Independent
Category: Informational C. Pignataro, Ed.
ISSN: 2070-1721 NC State University NCSU / Blue Fern
C. Westphal

L. Ciavaglia
Nokia
J. Tantsura
Nvidia
M-P. Odini
August 2025

Challenges and Opportunities in Management for Green Networking

Abstract

Reducing humankind's environmental footprint and making technology
more environmentally sustainable are among the biggest challenges of
our age. Networks play an important part in this challenge. On one
hand, they enable applications that help to reduce this footprint.
On the other hand, they significantly contribute to this footprint themselves in no
insignificant way.
themselves. Therefore, methods to make networking technology itself
"greener" and to manage and operate networks in ways that reduce
their environmental footprint without impacting their utility need to
be explored. This document outlines a corresponding set of
opportunities, along with associated research challenges, for
networking technology in general and management technology in
particular to become "greener", greener, i.e., more sustainable, with reduced
greenhouse gas emissions and less negative impact on the environment.

This document is a product of the Network Management Research Group
(NMRG) of the Internet Research Task Force (IRTF). This document
reflects the consensus of the research group. It is not a candidate
for any level of Internet Standard and is published for informational
purposes.

Status of This Memo

This document is not an Internet Standards Track specification; it is
published for informational purposes.

This document is a product of the Internet Research Task Force
(IRTF). The IRTF publishes the results of Internet-related research
and development activities. These results might not be suitable for
deployment. This RFC represents the consensus of the Network
Management Research Group of the Internet Research Task Force (IRTF).
Documents approved for publication by the IRSG are not candidates for
any level of Internet Standard; see Section 2 of RFC 7841.

Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9845.

This document is subject to BCP 78 and the IETF Trust's Legal
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(https://trustee.ietf.org/license-info) in effect on the date of
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Table of Contents

1. Introduction
1.1. Motivation
1.2. Approaching the Problem
1.3. Structuring the Problem Space
2. Definitions and Acronyms
3. Network Energy Consumption Characteristics and Implications
4. Challenges and Opportunities - Equipment Level
4.1. Hardware and Manufacturing
4.2. Visibility and Instrumentation
5. Challenges and Opportunities - Protocol Level
5.1. Protocol Enablers for Carbon Optimization Mechanisms
5.2. Protocol Optimization
5.3. Data Volume Reduction
5.4. Network Addressing
6. Challenges and Opportunities - Network Level
6.1. Network Optimization and Energy/Carbon/Pollution-Aware
Networking
6.2. Assessing Carbon Footprint and Network-Level
Instrumentation
6.3. Dimensioning and Peak Shaving
6.4. Convergence Schemes
6.5. The Role of Topology
7. Challenges and Opportunities - Architecture Level
8. Conclusions
9. IANA Considerations
10. Security Considerations
11. Informative References
Acknowledgments
Contributors
Authors' Addresses

1. Introduction

1.1. Motivation

Climate change and the need to curb greenhouse gas (GHG) emissions
have been recognized by the United Nations and by most governments as
one of the big challenges of our time. As a result, curbing those
emissions is becoming increasingly important for society and for many
industries. The networking industry is no exception.

The science behind greenhouse gas emissions and their relationship
with climate change is complex. However, there is overwhelming
scientific consensus pointing toward a clear correlation between
climate change and a rising amount of greenhouse gases in the
atmosphere. When we say 'greenhouse gases' or GHG, we are referring
to gases in the Earth’s atmosphere that trap heat and contribute to
the greenhouse effect. They include carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O), and Fluorinated gases (as covered under
the Kyoto Protocol and Paris Agreement). In terms of emissions from
human activity, the dominant greenhouse gas is CO2; consequently, it
often becomes shorthand for “all GHGs”. However, other gases are also
converted into “CO2-equivalents”, or CO2e. One greenhouse gas of
particular concern, but by no means the only one, is carbon dioxide
(CO2). Carbon dioxide is emitted in the process of burning fuels to
generate energy that is used, for example, to power electrical
devices such as networking equipment. Notable here is the use of
fossil fuels (such as oil, which releases CO2 that has long been
removed from the earth's atmosphere), as opposed to the use of
renewable or sustainable fuels that do not "add" to the amount of CO2
in the atmosphere. There are additional gases associated with
electricity generation, in particular methane (CH4) and nitrous oxide
(N2O). Although they exist in smaller quantities, they have an even
higher Global Warming Potential (GWP).

Greenhouse gas emissions are in turn correlated with the need to
power technology, including networks. Reducing those emissions can
be achieved by reducing the amount of fossil fuels needed to generate
the energy that is needed to power those networks. This can be
achieved by improving the energy mix to include increasing amounts of
low-carbon and/or renewable (and hence sustainable) energy sources,
such as wind or solar. It can also be achieved by increasing energy
savings and improving energy efficiency so that the same outcomes are
achieved while consuming less energy in the first place.

The amount of greenhouse gases that an activity adds to the
atmosphere, such as CO2 that is emitted in burning fossil fuels to
generate the required energy, is also referred to as the "greenhouse
footprint" or the "carbon footprint" (accounting for greenhouses greenhouse gases
other than CO2 in terms of CO2 equivalents). Reducing this footprint
to net zero is hence a major sustainability goal. However,
sustainability encompasses other factors beyond carbon, such as the
sustainable use of other natural resources, the preservation of
natural habitats and biodiversity, and the avoidance of any form of
pollution.

In the context of this document, we refer to networking technology
that helps to improve its own networking sustainability as "green".
Green, in that sense, includes technology that helps to lower
networking's greenhouse gas emissions including the carbon footprint,
which in turn includes technology that helps increase efficiency and
realize energy savings as well as facilitates managing networks
toward a stronger use of renewables.

Arguably, networks can already be considered a "green" green technology in
that networks enable many applications that allow users and whole
industries to save energy and thus become environmentally more
sustainable in a significant way. For example, they allow (at least
to an extent) to substitute travel with teleconferencing. They
enable many employees to work from home and telecommute, thus
reducing the need for actual commuting. IoT applications that
facilitate automated monitoring and control from remote sites help
make agriculture more sustainable by minimizing the usage of water,
fertilizer, and land area. land. Networked smart buildings allow for greater
energy optimization and sparser use of lighting and HVAC (heating,
ventilation, air conditioning) than their non-networked, not-so-smart
counterparts. That said, calculating precise benefits in terms of
net sustainability contributions and savings is complex, as a
holistic picture involves many effects including substitution effects
(perhaps saving on emissions caused by travel but incurring
additional costs associated with additional home office use) as well
as behavioral changes (perhaps a higher number of meetings than if
travel were involved).

The IETF has recently initiated a reflection on the energy cost of
hosting meetings three times a year (see [IETF-Net0]). It conducted
a study of the carbon emissions of a typical meeting and found out
that 99% of the emissions were due to air travel. In the same vein,
[Framework] compared an in-person with a virtual meeting and found a
reduction in energy of 66% for a virtual meeting. These findings
confirm that networking technology can reduce emissions when acting
as a virtual substitution for physical events.

That said, networks themselves consume significant amounts of energy.
Therefore, the networking industry has an important role to play in
meeting sustainability goals and not just by enabling others to
reduce their reliance on energy but by also reducing its own. Future
networking advances will increasingly need to focus on becoming more
energy efficient and reducing the carbon footprint, for reasons of
both corporate responsibility and economics. This shift has already
begun, and sustainability is becoming an important concern for
network providers. In some cases, such as in the context of
networked data centers, the ability to procure enough energy becomes
a bottleneck, prohibiting further growth, and greater sustainability
thus becomes a business necessity.

For example, in its annual report, Telefónica reports that in 2021,
its network's energy consumption per petabyte (PB) of data amounted
to 54 megawatt-hours (MWh) [Telefonica2021]. This rate has been
dramatically decreasing (by a factor of seven over six years),
although gains in efficiency are being offset by simultaneous growth
in data volume. The same report states that an important corporate
goal is continuing on that trajectory and aggressively reducing
overall carbon emissions further.

1.2. Approaching the Problem

An often-considered gain

One way in networking which gains in network sustainability can be made
with regards achieved
involves reducing the amount of energy needed to provide
communication services and improving the efficiency with which with
networks utilize power during their use phase, reducing the amount of energy that is
required to provide communication services. phase. However, for a
holistic approach, other aspects need to be considered as well.

The environmental footprint is not determined by energy consumption
alone. The sustainability of power sources needs to be considered as
well. A deployment that includes devices that are less energy
efficient but powered by a sustainable energy source can arguably be
considered "greener" greener than a deployment that includes highly efficient
devices that are powered by diesel generators. In fact, in the same
Telefónica report mentioned earlier, extensive reliance on renewable
energy sources is emphasized.

Similarly, deployments can take other environmental factors into
account that affect the carbon footprint. For example, deployments
where the need for cooling is reduced or where excessive heat
generated by equipment can be put to a productive use will be
considered greener than deployments where this is not the case.
Examples include deployments in cooler natural surroundings (e.g., in
colder climates) where that is an option. Likewise, manufacturing
and recycling networking equipment are also part of the
sustainability equation, as the production itself consumes energy and
results in a carbon cost embedded as part of the device itself.
Extending the lifetime of equipment may in many cases be preferable
over replacing it earlier with equipment that is slightly more energy
efficient, but that requires the embedded carbon cost to be amortized
over a much shorter period of time.

Management

Network management has an outsized role to play in approaching those
problems. To reduce the amount of energy used, network providers
need to maximize ways in which they the use of scarce resources and eliminate the use of unneeded resources.
resources that are not strictly needed. They need to optimize the
way in which networks are deployed, which resources are placed where,
and how equipment lifecycles and upgrades are being managed -- all of
which constitute classic operational problems. As best practices,
methods, and algorithms are developed, they need to be automated to
the greatest extent possible, migrated over time into the network,
and performed on increasingly short timescales, transcending
management and control planes.

1.3. Structuring the Problem Space

From a technical perspective, multiple vectors along which networks
can be made "greener" greener should be considered:

* Equipment level:

Perhaps the most promising vector for improving networking
sustainability concerns the network equipment itself. At the most
fundamental level, networks (even softwarized ones) involve
appliances, i.e., equipment that relies on electrical power to
perform its function. There are two distinct layers with
different opportunities for improvement:

- Hardware: Reducing embedded carbon during material extraction
and manufacturing, manufacturing; improving energy efficiency, efficiency and reducing
energy consumption during operations, operations; and increasing reuse, repurpose,
repurposing, and
recycle motions. recycling.

- Software: Improving software energy efficiency, maximizing
utilization of processing devices, and allowing for software to
interact with hardware to improve sustainability.

Beyond making network appliances merely more energy efficient,
there are other important ways in which equipment can help
networks become greener. This includes aspects such as supporting
port power-saving modes or down-speeding links to reduce power
consumption for resources that are not fully utilized. To fully
tap into the potential of such features, it features requires accompanying
management functionality, for example, example to determine when it is
"safe" to down-speed a link or to enter a power-saving power saving mode, and
to maximize
operate the conditions when network in such a way that action is appropriate. conditions to do so are
maximized.

Most importantly, from a management perspective, improving
sustainability at the equipment level involves providing
management instrumentation that allows for precise monitoring and
managing power usage and doing so at different levels of
granularity, for example, accounting separately for the
contributions of CPU, memory, and different ports. This enables
(for example) controller applications to optimize energy usage
across the network and to leverage control loops to assess the
effectiveness (e.g., in terms of reducing power use) of the
measures that are taken.

As a side note, the terms "device" and "equipment", as used in the
context of this document, are used to refer to networking
equipment. We are not taking into consideration end-user devices
and endpoints such as mobile phones or computing equipment.

* Protocol level:

Energy-efficiency and greenness "greenness" are aspects that are rarely
considered when designing network protocols. This suggests that
there may be plenty of untapped potential. Some aspects involve
designing protocols in ways that reduce the need for redundant or
wasteful transmission of data, allowing not only for better
network utilization but for greater goodput per unit of energy
being consumed. Techniques might include approaches that reduce
the "header tax" incurred by payloads as well as methods resulting
in the reduction of wasteful retransmissions. Similarly, there
may be cases where chattiness of protocols may be preventing
equipment from going into sleep mode. Designing protocols that
reduce chattiness in such scenarios, for example, that reduce
dependence on periodic updates or heartbeats, may result in
greener outcomes. Likewise, aspects such as restructuring
addresses in ways that minimize the size of lookup tables,
associated memory sizes, and hence energy use, can play a role as
well.

Another role of protocols concerns the enabling of management
functionality to improve energy efficiency at the network level,
such as discovery protocols that allow for quick adaptation to
network components being taken dynamically into and out of service
depending on network conditions, as well as protocols that can
assist with functions such as the collection of energy telemetry
data from the network.

* Network level:

Perhaps the greatest opportunities to realize power savings exist
at the level of the network as whole. Many of these opportunities
are directly related to management functionality. For example,
optimizing energy efficiency may involve directing traffic in such
a way that it allows the isolation of equipment that might not be
needed at certain moments so that it can be powered down or
brought into power-saving mode. By the same token, traffic should
be directed in a way that requires bringing additional equipment
online or out of power-saving mode in cases where alternative
traffic paths are available for which the incremental energy cost
would amount to zero. Likewise, some networking devices may have
a lower sustainability rating, be
rated less "green" and more power-intensive than others energy-efficient, or may be
powered by less-sustainable energy sources. sources than others. Their use
might be avoided except during periods of peak capacity demands.
Generally, incremental carbon emissions can be viewed as a cost
metric that networks should strive to minimize and consider as
part of routing and network path optimization.

* Architecture level:

The current network architecture supports a wide range of
applications but does not consider energy efficiency as one of its
design parameters. One can argue that the most energy efficient
shift of the last two decades has been the deployment of Content
Delivery Network overlays: while these were set up to reduce
latency and minimize bandwidth consumption, from a network
perspective, retrieving the content from a local cache is also
much greener. What other architectural shifts can produce energy
consumption reduction?

In this document, we will explore each of those vectors in further
detail and attempt to articulate specific challenges that could make
a difference when addressed. As our starting point, we borrow some
material from "Challenges and Opportunities in Green Networking"
[GreenNet22]. For this document, this material has been both
expanded (for example, in terms of some of the opportunities) and
pruned (for example, in terms of background on prior scholarly work).

This document is a product of the Network Management Research Group
(NMRG) of the Internet Research Task Force (IRTF). This document
reflects the consensus of the research group and was discussed and
presented multiple times, each time receiving positive feedback and
no objections. It is not a candidate for any level of Internet
Standard and is published for informational purposes.

2. Definitions and Acronyms

Below you find acronyms used in this document:

Carbon Footprint: As used in this document, the amount of carbon
emissions associated with the use or deployment of technology,
usually correlated with the amount of energy consumption

CDN: Content Delivery Network

CPU: Central Processing Unit (that is, the main processor in a
server)

DC: Data Center

FCT: Flow Completion Time

GHG: Greenhouse Gas

GPU: Graphical Processing Unit

HVAC: Heating, Ventilation, Air Conditioning

ICN: Information-Centric Network

IGP: Interior Gateway Protocol

IoT: Internet of Things

IPU: Infrastructure Processing Unit

LEED: Leadership in Energy and Environmental Design (a green
building rating system)

LEO: Low Earth Orbit

LPM: Longest Prefix Match (a method to look up prefixes in a
forwarding element)

MPLS: Multiprotocol Label Switching

MTU: Maximum Transmission Unit (the largest packet size that can be
transmitted over a network)

NIC: Network Interface Card

QoE: Quality of Experience

QoS: Quality of Service

QUIC: Quick UDP Internet Connections the name of a UDP-based, stream-multiplexing, encrypted
transport protocol. [RFC9000]

SDN: Software-Defined Networking

SNIC: Smart NIC

TCP: Transport Control Protocol

TE: Traffic Engineering

TPU: Tensor Processing Unit

WAN: Wide Area Network

3. Network Energy Consumption Characteristics and Implications

The carbon footprint and, with it, greenhouse gas emissions are
determined by several factors. A main factor is network energy
consumption, as the energy consumed can be considered a proxy for the
burning of fuels required for corresponding power generation.
Network energy consumption by itself does not tell the whole story,
as it does not take the sustainability of energy sources and the
energy mix into account. Likewise, there are other factors such as
the carbon cost expended in the manufacturing of networking hardware.
Nonetheless, network energy consumption is an excellent predictor of
a carbon footprint and its reduction, which is key to sustainable
solutions. Hence, exploring possibilities to improve energy
efficiency is a key factor for greener, more sustainable, less
carbon-intensive networks.

It is important to understand some of the characteristics of power
consumption by networks and which aspects contribute the most. This
helps to identify where the greatest potential is, not just for power
savings but also for sustainability improvements.

Power is ultimately drawn by devices. Devices are not monoliths but
are composed of multiple components. The power consumption of the
device can be divided into the consumption of the core device -- the
backplane and CPU, if you will -- as well as additional consumption
incurred per port and line card. In addition, the GPU and TPU may be
used in the network and may have different power consumption
profiles. Furthermore, it is important to understand the difference
between power consumption when a resource is idling versus when it is
under load. This helps to understand the incremental cost of
additional transmission versus the initial cost of transmission.

In typical networking devices, only roughly half of the energy
consumption is associated with the data plane [Bolla2011energy]. An
idle base system typically consumes more than half of the energy that
the same system would consume when running at full load [Chabarek08]
[Cervero15]. Generally, the cost of sending the first bit is very
high, as it requires powering up a device, port, etc. The
incremental energy cost of transmission of additional bits (beyond
the first) is many orders of magnitude lower. Likewise, the
incremental cost of the incremental CPU and memory needed to process
additional packets becomes fairly negligible.

This means that a device's energy consumption does not increase
linearly with the volume of forwarded traffic. Instead, it resembles
a step function in which energy consumption stays roughly the same up
to a certain volume of traffic, followed by a sudden jump when
additional resources need to be procured to support a higher volume
of traffic.

By the same token, it is generally more energy efficient to transmit
a large volume of data in one burst (and subsequently turn off or
down-speed the interface when idling) than to continuously transmit
at a lower rate. In that sense, it can be the duration of the
transmission that dominates the energy consumption -- not the actual
data rate.

The implications on green networking from an energy-savings
standpoint are significant. Of utmost importance are schemes that
allow for "peak shaving": networks are typically dimensioned for
periods of peak demand and usage, yet any excess capacity during
periods of non-peak usage does not result in corresponding energy
savings. Peak shaving techniques that reduce peak traffic spikes and
waste during non-peak periods may result in outsize sustainability
gains. Peak shaving could be accomplished by techniques such as
spreading spikes out over geographies (e.g., routing traffic across
more costly but less utilized routes) or over time (e.g., postponing
and buffering non-urgent traffic).

Likewise, large gains can be made whenever network resources can
effectively be taken offline for at least some of the time, managing
networks in a way that enables resources to be removed from service
so they can be powered down (or put into a more energy-saving state,
such as when down-speeding ports) while not needed. Of course, any
such methods need to take into account the overhead of taking
resources offline and bringing them back online. This typically
takes some amount of time, requiring accurate predictive capabilities
to avoid situations in which network resources are not available at
times when they would be needed. In addition, there is additional
overhead, such as synchronization of state, to be accounted for.

At the same time, any non-idle resources should be utilized to the
greatest extent possible, as the incremental energy cost is
negligible. Of course, this needs to occur while still taking other
operational goals into consideration, such as protection against
failures (allowing for readily available redundancy and spare
capacity in case of failure) and load balancing (for increased
operational robustness). As data transmission needs tend to
fluctuate wildly and occur in bursts, any optimization schemes need
to be highly adaptable and allow control loops at very fast time
scales.

Similarly, for applications where this is possible, it may be
desirable to replace continuous traffic at low data rates with
traffic that is sent in bursts at high data rates in order to
potentially maximize the time during which resources can be idled.

As a result, emphasis needs to be given to technology that allows, enables,
for example, (at the device level) very efficient and rapid discovery, monitoring, and
control of networking resources so that
they can resources. This allows devices to be
dynamically taken offline or brought back in service online without (at the network level) requiring an extensive convergence of
network-level state across the network convergence, route recalculation, or a recalculation of routes and other
optimization problems, and (at
complex optimizations at the network equipment level) support level. To facilitate such
schemes, rapid power cycle and initialization schemes. schemes should be
supported at the device level. There may be some lessons that can be
applied here from IoT, which has long had to contend with power-constrained power-
constrained end devices that need to spend much of their time in
power-saving states to conserve battery.

4. Challenges and Opportunities - Equipment Level

We are categorizing challenges and opportunities to improve
sustainability at the network equipment level along the following
lines:

* Hardware and manufacturing: Related opportunities are arguably
among the most obvious and perhaps "largest". However, solutions
here lie largely outside the scope of networking researchers.

* Visibility and instrumentation: Instrumenting equipment to provide
visibility into how they consume energy is key to management
solutions and control loops to facilitate optimization schemes.

4.1. Hardware and Manufacturing

Perhaps the most obvious opportunities to make networking technology
more energy efficient exist at the equipment level. After all,
networking involves physical equipment to receive and transmit data.
Making such equipment more power efficient, having it dissipate less
heat to consume less energy and reduce the need for cooling, making
it eco-friendly to deploy, sourcing
sustainable materials, and facilitating the recycling of equipment at
the end of its lifecycle
-- all contribute to making networks greener.
Reducing the energy usage of transmission technology, from wireless
(antennas) to optical (lasers), is a strategy that is unique to
networking.

One critical aspect of the energy cost of networking is the cost to
manufacture and deploy the networking equipment. In addition, even
the development process itself comes with its own carbon footprint.
This is outside of the scope of this document: we only consider the
energy cost of running the network during the operational part of the
equipment's lifecycle. However, a holistic approach would include
the embedded energy that is included in the networking equipment. As
part of this, aspects such as the impact of deploying new protocols
on the rate of obsolescence of existing equipment should be
considered. For instance, incremental approaches that do not require
replacing equipment right away -- or even that extend the lifetime of
deployed equipment -- would have a lower energy carbon footprint. This is
one important benefit also of technologies such as Software-Defined
Networking and network function virtualization, as they may allow
support for new networking features through software updates without
requiring hardware replacements.

[Emergy] describes an attempt to compute not only the energy of
running a network but also the energy embedded into manufacturing the
equipment. This is denoted by "emergy", a portmanteau for embedded
energy. Likewise, [Junkyard] describes an approach to recycling
equipment and a proof of concept using old mobile phones recycled
into a "junkyard" data center.

One trade-off to consider at this level is the selection of a
platform that can be hardware-optimized for energy efficiency versus
a platform that is versatile and can run multiple functions. For
instance, a switch could run on an efficient hardware platform or run
as a software module (container) over some multipurpose platform.
While the first one is operationally more energy efficient, it may
have a higher embedded energy from a smaller scale, a less efficient
production process, as well as a shorter shelf life once new
functions need to be added to the platform.

4.2. Visibility and Instrumentation

Beyond "first-order" opportunities, as outlined in Section 4.1,
network equipment just as importantly plays a role in enabling and
supporting green networking at other levels. Of prime importance is
the equipment's ability to provide visibility to the management and
control planes into its current energy usage. Such visibility
enables control loops for energy optimization schemes, allowing
applications to obtain feedback regarding the energy implications of
their actions, from setting up paths across the network that require
the least incremental amount of energy to quantifying metrics related
to energy cost to optimize forwarding decisions. Absent an actual
measurement of energy usage (and until such measurement is put in
place), the network equipment could advertise some proxy of its power
consumption. For example, it could use a labeling scheme of silver,
gold, or platinum similar to the LEED sustainability metric in
building codes, or the Energy Star label in home appliances, or a
description of the type of the device as using CPU vs. GPU vs. TPU
processors with different power profiles.

One prerequisite to such schemes is to have proper instrumentation in
place that allows for monitoring current power consumption at the
level of networking devices as a whole, line cards, and individual
ports. Such instrumentation should also allow for assessing the
energy efficiency and carbon footprint of the device as a whole. In
addition, it will be desirable to relate this power consumption to
data rates and to current traffic, for example, to indicate current
energy consumption relative to interface speeds, as well as for
incremental energy consumption that is expected for incremental
traffic (to aid control schemes that aim to "shave" power off current
services or to minimize the incremental use of power for additional
traffic). This is an area where the current state of the art is
sorely lacking, and standardization lags behind. For example, as of
today, standardized YANG data models [RFC7950] for network energy
consumption that can be used in conjunction with management and
control protocols have yet to be defined.

To remedy this situation, efforts to define sets of green networking
metrics [GREEN_METRICS] as well as YANG data models that include
objects that provide visibility into power measurements (e.g.,
[POWER_YANG]) were underway in 2024. Agreed sets of metrics and
corresponding data models will provide the basis for further steps,
beginning with their implementation as part of management and control
instrumentation.

Instrumentation should also take into account the possibility of
virtualization, introducing layers of indirection to assess the
actual energy usage. For example, virtualized networking functions
could be hosted on containers or virtual machines that are hosted on
a CPU in a data center instead of a regular network appliance such as
a router or a switch, leading to very different power consumption
characteristics. For example, a data center CPU's power consumption
could be more efficient and more proportional to actual CPU load.
Instrumentation needs to reflect these facts and facilitate
attributing power consumption in a correct manner.

Beyond monitoring and providing visibility into power consumption,
control knobs are needed to configure energy-saving policies. For
instance, power-saving modes are common in endpoints (such as mobile
phones or notebook computers) but sorely lacking in networking
equipment.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Basic equipment categorization as "energy efficient" (or not) as a
first step to identify immediate potential improvements, akin to
the Energy Star program from the US's Environmental Protection
Agency.

* Equipment instrumentation advances for improved energy awareness;
definition and standardization of granular management information.

* Virtualized energy and carbon metrics and assessment of their
effectiveness in solutions that optimize carbon footprints in
virtualized environments (including SDN, network slicing, network
function virtualization, etc.).

* Certification and compliance assessment methods that ensure that
green instrumentation cannot be manipulated to give false and
misleading data.

* Methods that account allow for equipment that powers an the energy mix, mix of the power sources that
are used to power equipment to be taken into account, in order to
facilitate solutions that optimize the actual carbon footprint and
minimize pollution beyond mere energy efficiency [Hossain2019].

5. Challenges and Opportunities - Protocol Level

There are several opportunities to improve network sustainability at
the protocol level, which can be categorized as follows. The first
and arguably most impactful category concerns protocols that enable
carbon footprint optimization schemes at the network level and
management towards those goals. Other categories concern protocols
designed to optimize data transmission rates under energy
considerations, protocols designed to reduce the volume of data to be
transmitted, and protocol aspects related to network addressing
schemes. While those categories may be less impactful, even areas
with smaller gains should be explored.

There is also substantial work in the area of IoT, which has had to
contend with energy-constrained devices for a long time. Much of
that work was motivated not by sustainability concerns but practical
concerns such as battery life. However, many aspects appear to also
apply in the context of sustainability, such as reducing chattiness
to allow IoT equipment to go into low-power mode. Accordingly, there
is an opportunity to extend IoT work to more generalized scenarios.
The use of power-constrained protocols in the wider Internet happens
regularly. For instance, ARM-based chipsets initially designed for
energy efficiency in battery-operated mobile devices have been
embraced in data centers for a similar trajectory.

5.1. Protocol Enablers for Carbon Optimization Mechanisms

As discussed in Section 6, energy-aware and pollution-aware schemes
can help improve network sustainability but require awareness of
related data. To facilitate such schemes, protocols are needed that
are able to discover what links are available along with their energy
efficiency. For instance, links may be turned off in order to save
energy and turned back on based upon the elasticity of the demand.
Protocols should be devised to discover when this happens and to have
a dynamic view of the topology that keeps up with frequent updates
due to power cycling of the network resources.

Also, protocols are required to quickly converge onto an energy-
efficient path once a new topology is created by turning links on/
off. Current routing protocols may provide for fast recovery in the
case of failure. However, failures are hopefully relatively rare
events, while we expect an energy-efficient network to aggressively
try to turn off links. There may be synergies with Time-Variant
Routing [TVR_REQS] that can be explored, in which the topology varies
over time with nodes and links turned on or off according to a
schedule. There may be overlaps in use cases, for example, when
regular changes in the energy mix (and hence carbon footprint) of
some nodes occur that coincide with the time of day (such as
switching from solar to fossil fuels at night).

Some mechanism is needed to present to the management layer a view of
the network that identifies opportunities to turn off resources
(e.g., routers or links) while still providing an acceptable level of
Quality of Experience (QoE) to the users. This gets more complex as
the level of QoE shifts from the current best-effort delivery model
to more sophisticated mechanisms with, for instance, latency,
bandwidth, or reliability guarantees.

Similarly, schemes might be devised in which links across paths with
a favorable energy mix are preferred over other paths. This implies
that the discovery of topology should be able support corresponding
parameters. More generally speaking, any mechanism that provides
applications with network visibility is a candidate for
scrutinization as to whether it should be extended to provide support
for sustainability-related parameters.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Protocol advances to enable rapidly taking down, bringing back
online, and discovering availability and power-saving status of
networking resources while minimizing the need for reconvergence
and propagation of state.

* An assessment of which protocols could be extended with energy-
and sustainability-related parameters in ways that would enable
"greener"
greener networking solutions, and an exploration of those
solutions.

5.2. Protocol Optimization

The second category involves designing protocols in such a way that
the rate of transmission is chosen to maximize energy efficiency.
For example, Traffic Engineering (TE) can be manipulated to impact
the rate adaptation mechanism [Ren2018jordan]. By choosing where to
send the traffic, TE can artificially congest links so as to trigger
rate adaptation and therefore reduce the total amount of traffic.
Most TE systems attempt to minimize Maximum Link Utilization but
energy-saving mechanisms could decide to do the opposite (i.e.,
maximize Minimum Link Utilization) and attempt to turn off some
resources to save power.

Another example is to set up the proper rate of transmission to
minimize the flow completion time (FCT) so as to enable opportunities
to turn off links. In a wireless context, [TradeOff] studies how
setting the proper initial value for the congestion window can reduce
the FCT and therefore allow the equipment to go faster into a low-
energy mode. By sending the data faster, the energy cost can be
significantly reduced. This is a simple proof of concept, but
protocols that allow for turning links into a low-power mode by
transmitting the data over shorter periods could be designed for
other types of networks beyond Wi-Fi access. This should be done
carefully: in an extreme case, a sudden very high rate of transmission over a short
period of time may create bursts that the network would need to
accommodate, with all attendant complications of bursty traffic. We
conjecture there is a sweet spot between trying to complete flows
faster while controlling for burstiness in the network. It is
probably advisable to attempt to send traffic paced yet in bulk
rather than spread out over multiple round trips. This is an area of
worthwhile exploration.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Protocol advances that allow greater control over traffic pacing
to account for fluctuations in carbon cost, i.e., control knobs to
"bulk up" transmission over short periods or to smooth it out over
longer periods.

* Protocol advances for optimizing link utilization according to
different goals and strategies (including maximizing Minimal Link
Utilization vs. minimizing Maximal Link Utilization, etc.)

* Assessments of the carbon impact of such strategies.

5.3. Data Volume Reduction

The third category involves designing protocols in such a way that
they reduce the volume of data that needs to be transmitted for any
given purpose. Loosely speaking, by reducing this volume, more
traffic can be served by the same amount of networking
infrastructure, hence reducing overall energy consumption.
Possibilities here include protocols that avoid unnecessary
retransmissions. At the application layer, protocols may also use
coding mechanisms that encode information close to the Shannon limit. limit
[Shannon]. Currently, most of the traffic over the Internet consists
of video
streaming, and video streaming. Video encoders are already quite efficient and
keep improving all the time. This results in energy savings as one
of many advantages, although benefits, even if some of course the those savings are may be partially
offset by increasingly higher resolution. It is not clear that the
extra work to achieve higher compression ratios for the payloads
results in a net energy gain: what is saved over the network may be
offset by the compression/decompression effort. Further research on
this aspect is necessary.

At the transport protocol layer, TCP and to some extent QUIC react to
congestion by dropping packets. This is an extremely energy
inefficient method to signal congestion because (a) the network has
to wait one RTT to be aware that the congestion has occurred, and (b)
the effort to transmit the packet from the source up until it is
dropped ends up being wasted. This calls for new transport protocols
that react to congestion without dropping packets. ECN [RFC2481] [RFC3168] is
a possible solution, however, it is not widely deployed. DCTCP
[Alizadeh2010DCTCP] is tuned for data centers; Low Latency, Low Loss,
and Scalable Throughput (L4S) is an attempt to port similar
functionality to the Internet [RFC9330]. Qualitative Communication
[QUAL] [Westphal2021qualitative] allows the nodes to react to
congestion by dropping only some of the data in the packet, thereby
only partially wasting the resource consumed by transmitted the
packet up to that point. Novel transport protocols for the WAN can
ensure that no energy is wasted transmitting packets that will be
eventually dropped.

Another solution to reduce the bandwidth of network protocols is by
reducing their header tax, for example, by applying header
compression. An example in IETF is RObust Header Compression (ROHC)
[RFC3095]. Again, reducing protocol header size saves energy to
forward packets, but at the cost of maintaining a state for
compression/decompression, plus computing these operations. The gain
from such protocol optimization further depends on the application
and whether it sends packets with (a) large payloads close to the
MTU, thus limiting the header tax and any savings, or (b) very small
payload size, thus increasing the header tax and the savings.

An alternative to reducing the amount of protocol data is to design
routing protocols that are more efficient to process at each node.
For instance, path-based forwarding/labels such as MPLS [RFC3031]
facilitate the next hop lookup, thereby reducing the energy
consumption. It is unclear if some state at router to speed up
lookup is more energy efficient than "no state + lookup", which is
more computationally intensive. Other methods to speed up a next-hop
lookup include geographic routing (e.g., [Herzen2011PIE]). Some
network protocols could be designed to reduce the next hop lookup
computation at a router. It is unclear whether Longest Prefix Match
(LPM) is energy efficient or a significant energy burden for router
operation.

Beyond the volume of data itself, another consideration is the number
of messages and chattiness of the protocol. Some protocols rely on
frequent periodic updates or heartbeats, which may prevent equipment
from going into sleep mode. In such cases, it makes sense to explore
the use of feasible alternatives that rely on different communication
patterns and fewer messages.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Assessments of energy-related trade-offs regarding protocol design
space and trade-offs, such as maintaining state versus more
compact encodings, or extra computation for transcoding operations
versus larger data volume.

* Protocol advances for improving the ratio of goodput to throughput
and to reduce waste: reduction waste; this includes advances such as coding
improvements, reductions in header tax, in lower protocol verbosity, in
and reduced need for retransmissions, improvements in coding,
etc. retransmissions.

* Protocols that allow for managing transmission patterns in ways
that facilitate periods of link inactivity, such as burstiness and
chattiness.

5.4. Network Addressing

Network addressing is another way to shave off energy usage from
networks. Address tables can get very large, resulting in large
forwarding tables that require considerable amount of memory, in
addition to large amounts of state that needs needing to be maintained and
synchronized. From an energy footprint perspective, Memory as well as the processing needed to maintain
and synchronize state both can be
considered wasteful consume energy. Exploring ways to reduce
the amount of memory and offer synchronization of state that is required
offers opportunities for improvement. to reduce energy use. At the protocol level,
rethinking how addresses are structured can allow for flexible
addressing schemes that can be exploited in network deployments that
are less energy-intensive by design. This can be complemented by
supporting clever address allocation schemes that minimize the number
of required forwarding entries as part of deployments.

Alternatively, the addressing could be designed to allow for more
efficient processing than LPM. For instance, a geographic type of
addressing (where the next hop is computed as a simple distance
calculation based on the respective position of the current node, of
its neighbors and of the destination) [Herzen2011PIE] could be
potentially more energy efficient.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Devise methods to assess the magnitude of the carbon footprint
that is associated with addressing schemes.

* Devise methods to improve addressing schemes, as well as address
assignment schemes, to minimize their footprint.

6. Challenges and Opportunities - Network Level

6.1. Network Optimization and Energy/Carbon/Pollution-Aware Networking

Networks have been optimized for many years under many criteria, for
example, to optimize (maximize) network utilization and to optimize
(minimize) cost. Hence, it is straightforward to add optimization
for "greenness" greenness (including energy efficiency, power consumption, carbon
footprint) as important criteria.

This includes assessing the carbon footprints of paths and optimizing
those paths so that overall footprint is minimized, then applying
techniques such as path-aware networking or segment routing [RFC8402]
to steer traffic along those paths. (As mentioned earlier, other
proxy measures could be used for carbon footprint, such as energy-
efficiency ratings of traversed equipment.) It also includes aspects
such as considering the incremental carbon footprint in routing
decisions. Optimizing cost has a long tradition been an area of focus in
networking; many of the existing mechanisms can be leveraged for
greener networking simply by introducing the carbon footprint as a
cost factor. Low-
hanging Low-hanging fruit includes adding carbon-related
parameters as a cost parameter in control planes, whether distributed
(e.g., IGP) or conceptually centralized via SDN controllers.
Likewise, there are opportunities in right-placing to correctly place functionality in
the network. network for optimal effectiveness. An example is placement of
virtualized network functions in carbon-
optimized ways, i.e., carbon-optimized ways. for exmaple,
virtualized network functions can be cohosted on fewer servers to
achieve higher server utilization, which is more effective from an
energy and carbon perspective than larger numbers of servers with
lower utilization. Likewise, they can be placed in close proximity
to each other in order to avoid unnecessary overhead in long-distance
control traffic.

Other opportunities concern adding carbon awareness to dynamic path
selection schemes. This is sometimes referred to as "energy-aware
networking" (or "pollution-aware networking" [Hossain2019] or
"carbon-aware networking", when parameters beyond simply energy
consumption are taken into account). Again, considerable energy
savings can potentially be realized by taking resources offline
(e.g., putting them into power-saving or hibernation mode) when they
are not needed under current network demand and load conditions.
Therefore, weaning such resources from traffic becomes is an important
consideration for energy-efficient traffic steering. This approach
contrasts and indeed conflicts with existing schemes that typically
aim to to create redundancy and load-balance traffic across a network
to achieve even resource utilization. This usually occurs for important
reasons, such utilization across larger numbers of network
resources as making networks more resilient, optimizing a means to increase network resilience, optimize service
levels, and increasing ensure fairness. Thus, a big challenge is how
resource-weaning resource-
weaning schemes to realize energy savings can be accommodated without
cannibalizing other important goals, counteracting other established
mechanisms, or destabilizing the network.

An opportunity may lie in making a distinction between "energy modes"
of different domains. For instance, in a highly trafficked core, the
energy challenge is to transmit the traffic efficiently. The amount
of traffic is relatively fluid (due to multiplexing of multiple
sessions) and the traffic is predictable. In this case, there is no
need to optimize on a per-session basis or at a short timescale. In
the access networks connecting to that core, though, there are
opportunities for this fast convergence: traffic is much more bursty
and less predictable, and the network should be able to be more
reactive. Other domains such as DCs may have more variable workloads
and different traffic patterns.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Devise methods for carbon-aware traffic steering and routing;
treat carbon footprint as a traffic cost metric to optimize.

* Apply Machine Learning (ML) and AI methods to optimize networks
for carbon footprint; assess applicability of game theoretic
approaches.

* Articulate and, as applicable, moderate trade-offs between carbon
awareness and other operational goals such as robustness and
redundancy.

* Extend control plane protocols with carbon-related parameters.

* Consider security issues imposed by greater energy awareness, to
minimize the new attack surfaces that would allow an adversary to
turn off resources or to waste energy.

6.2. Assessing Carbon Footprint and Network-Level Instrumentation

As an important prerequisite to capture many of the opportunities
outlined in Section 6.1, good abstractions (and corresponding
instrumentation) for easily assessing energy cost and carbon
footprint will be required. These abstractions need to account for
not only the energy cost associated with packet forwarding across a
given path, but also the related cost for processing, for memory, and
for maintaining of state, to result in a holistic picture.

In many cases, optimization of carbon footprint has trade-offs that
involve not only packet forwarding but also aspects such as keeping
state, caching data, or running computations at the edge instead of
elsewhere. (Note: There may be a differential in running a
computation at an edge server vs. at a hyperscale DC. The latter is
often better optimized than the latter.) former.) Likewise, other aspects of
carbon footprint beyond mere energy-intensity should be considered.
For instance, some network segments may be powered by more
sustainable energy sources than others, and some network equipment
may be more environmentally friendly to build, deploy, and recycle,
all of which can be reflected in abstractions to consider.

Assessing carbon footprint at the network level requires
instrumentation that associates that footprint not just with
individual devices (as outlined in Section 4.2) but also with
concepts that are meaningful at the network level, i.e., to flows and
to paths. For example, it will be useful to provide visibility into
the carbon intensity of a path: Can the carbon cost of traffic
transmitted over the path be aggregated? Does the path include
outliers, i.e., segments with equipment with a particularly poor large
carbon footprint?

Similarly, how can the carbon cost of a flow be assessed? That might
serve many purposes beyond network optimization, e.g., introducing from the option to
introduce green billing and charging schemes, and raising schemes that account for the
amount of carbon-equivalent emissions that are attributed to the use
of communication services by particular users to the ability to raise
carbon awareness by end users.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Devise methods to assess, estimate, and predict the carbon
intensity of paths.

* Devise methods to account for the carbon footprint of flows and
networking services.

6.3. Dimensioning and Peak Shaving

As mentioned in Section 3, the overall energy usage of a network is
in large part determined by how the network is dimensioned,
specifically: which and how many pieces of network equipment are
deployed and turned on. A significant portion of energy is drawn
even when simply in idle state. Hence, minimizing the amount of
equipment that needs to be turned on in the first place presents one
of the biggest energy-saving opportunities.

Network deployments are generally dimensioned for periods of peak
traffic, resulting in excess capacity during periods of non-peak
usage that nonetheless consumes power. Shaving peak usage may thus
result in outsized sustainability gains, as it reduces energy usage
during peak traffic but, more importantly, waste during non-peak
periods.

While traffic volume is largely a function of demand traffic that
network providers have little influence over, some peak shaving can
nevertheless be accomplished by techniques such as spreading spikes
out over geographies (e.g., redirecting some traffic across more
costly but less utilized routes, particularly in cases when traffic
spikes are of a more local or regional nature) or over time (e.g.,
postponing non-urgent traffic, storing or buffering using edge clouds
or extra storage where feasible).

To make techniques effective, accurate learning and prediction of
traffic patterns are required. This includes the ability to perform
forecasting to ensure that additional resources can be spun up in
time should they be needed. Clearly, this presents interesting
challenges, yet also opportunities for technical advances to make a
difference.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Support methods for monitoring and forecasting traffic demand,
involving new mechanisms and/or performance improvements of
existing mechanisms to support the collection of telemetry and
generation of traffic matrices at very high velocity and scale.

* Additional methods for distributing traffic load evenly across the
network, i.e., load balancing on a network scale, and enablement
of those methods through control protocol extensions as needed.

6.4. Convergence Schemes

One set of challenges of carbon-aware networking concerns the fact
that many schemes result in much greater dynamicity and continuous
change in the network, as resources may be steered away from (when
possible) and then leveraged again (when necessary) in rapid
succession. This imposes significant stress on convergence schemes
that results in challenges to the scalability of solutions and their
ability to perform in a fast-enough manner. Network-wide convergence
imposes high cost and incurs significant delay and thus is not
susceptible to such schemes. In order to mitigate this problem,
mechanisms should be investigated that do not require convergence
beyond the vicinity of the affected network device. The impact of
churn needs to be minimized, especially in cases where central
network controllers (responsible for the configuration of paths and
the positioning of network functions and that aim for global
optimization) are involved. This means that, for example, discovery,
rediscovery, and update schemes need to be simplified, and extensive
recalculation (e.g., of routes and paths based on the current energy
state of the network) needs to be avoided.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Protocols that facilitate rapid convergence (per Section 5.1).

* Investigate methods that mitigate effects of churn, including
methods that maintain memory or state as well as methods relying
on prediction, inference, and interpolation.

6.5. The Role of Topology

One of the most important network management constructs is that of
the network topology. A network topology can usually be represented
as a database or as a mathematical graph, with vertices or nodes,
edges or links, representing networking nodes, links connecting their
interfaces, and all their characteristics. Examples of these network
topology representations include routing protocols' link-state
databases (LSDBs) and service function chaining graphs.

To add

As part of adding carbon and energy awareness into networks, the energy
proportionality of topologies directly supports it is
useful also for topology information to provide visibility into
energy consumption
sustainability data. Such capabilities can help to assess
sustainability of the network overall and improvements via automation. can enable automated
applications to improve it.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Embedding carbon and energy awareness into the representation of
topologies, whether considering IGP LSDBs and their
advertisements, BGP-LS (BGP Link-State), or metadata for the
rendering of service function paths in a service chain.

* Use of those carbon-aware attributes to optimize topology as a
whole under end-to-end energy and carbon considerations.

7. Challenges and Opportunities - Architecture Level

Another possibility to improve network energy efficiency is to
organize networks in a way that they allow important applications to
reduce energy consumption. Examples include facilitating retrieval
of content or performing computation in ways that minimize the amount
of communication needed in the first place, even if energy savings
within the network may be offset (at least in part) by additional
energy consumption elsewhere. The following examples suggest that it
may be worthwhile to reconsider the ways in which networks are
architected to minimize their carbon footprint.

For example, Content Delivery Networks (CDNs) have reduced the energy
expenditure of the Internet by downloading content near the users.
The content is sent only a few times over the WAN and then is served
locally. This shifts the energy consumption from networking to
storage. Further methods can reduce the energy usage even more
[Bianco2016energy] [Mathew2011energy] [Islam2012evaluating]. Whether
overall energy savings are net positive depends on the actual
deployment, but from the network operator's perspective, at least it
shifts the energy bill away from the network to the CDN operator.

While CDNs operate as an overlay, another architecture has been
proposed to provide the CDN features directly in the network --
namely, Information-Centric Networks [Ahlgren2012survey], also
studied in the ICNRG of the IRTF. However, this shifts the energy
consumption back to the network operator and requires some power-
hungry hardware, such as chips for larger name lookups and memory for
the in-network cache. As a result, it is unclear if there is an
actual energy gain from the dissemination and retrieval of content
within in-network caches.

Fog computing and placing intelligence at the edge are other
architectural directions for reducing the amount of energy that is
spent on packet forwarding and in the network. There again, the
trade-off is between performing computational tasks (a) in an energy-
optimized data center at very large scale (but requiring transmission
of significant volumes of data across many nodes and long distances)
versus (b) at the edge where the energy may not be used as
efficiently (less multiplexing of resources and inherently lower
efficiency of smaller sites due to their smaller scale) but the
amount of long-distance network traffic and energy required for the
network is significantly reduced. Softwarization, containers, and
microservices are direct enablers of such architectures. Their
realization will be further aided by the deployment of programmable
network infrastructure, such as Infrastructure Processing Units
(IPUs) or SmartNICs that offload some computations from the CPU onto
the NIC. However, the power consumption characteristics of CPUs are
different from those of NPUs; this is another aspect to be considered
in conjunction with virtualization.

Other possibilities are taking economic aspects into consideration,
such as providing incentives to users of networking services in order
to minimize energy consumption and emission impact. In
[Wolf2014choicenet], an example is provided that could be expanded to
include energy incentives.

Other approaches consider performing a late binding of the data and
the functions to be performed on it [Krol2017NFaaS]. The COINRG of
the IRTF focuses on similar issues. Jointly optimizing for the total
energy cost that takes into account networking as well as computing
(along with the different energy cost of computing in a hyperscale DC
vs. at an edge node) is still an area of open research.

In summary, rethinking the overall network (and networked
application) architecture can be an opportunity to significantly
reduce the energy cost at the network layer, for example, by
performing tasks that involve massive communications closer to the
user. To what extent these shifts result in a net reduction of
carbon footprint is an important question that requires further
analysis on a case-by-case basis.

The following summarizes some challenges and opportunities in this
space that can provide the basis for advances in greener networking:

* Investigate organization of networking architecture for important
classes of applications (e.g., content delivery, right-placing most suitable
placement of computational intelligence, intelligence and network functionality
within the network design, industrial operations and control,
massively distributed ML and AI) to optimize green footprint overall
sustainability and holistic approaches to trade-offs of trade off carbon
footprint with between forwarding, storage, and computation.

* Models to assess and compare alternatives in providing networked
services, e.g., evaluate carbon impact relative to where to
perform computation, what information to cache, and what
communication exchanges to conduct.

8. Conclusions

How to make networks "greener" greener and reduce their carbon footprint is an
important problem for the networking industry to address, both for
societal and for economic reasons. This document has highlighted a
number of the technical challenges and opportunities in that regard.

Of those, perhaps the key challenge to address right away is the
ability to expose at a fine granularity the energy impact of any
networking actions. Providing visibility into this will enable many
approaches to come towards a solution. It will be key to
implementing optimization via control loops that can assess the
energy impact of a decision taken. It will also help to answer
questions such as:

* Is caching (with the associated storage) better than
retransmitting from a different server (with the associated
networking cost)?
* Is compression more energy efficient once factoring in the
computation cost of compression vs. transmitting uncompressed
data?
* Which compression scheme is more energy efficient?
* Is energy saving of computing at an efficient hyperscale DC
compensated by the networking cost to reach that DC?
* Is the overhead of gathering and transmitting fine-grained energy
telemetry data offset by the total energy gain resulting from the
better decisions that this data enables?
* Is transmitting the energy cost needed to transmit data to a Low Earth Orbit
(LEO) satellite constellation compensated offset by the fact that once in the
constellation, the
constallation and any networking is fueled within it are powered by solar
energy?
* Is the energy cost of sending rockets to place routers in LEO
amortized over time?

Determining where the sweet spots are and optimizing networks along
those lines will be a key towards making networks "greener". greener. We expect
to see significant advances across these areas and believe that
researchers, developers, and operators of networking technology have
an important role to play in this.

9. IANA Considerations

This document has no IANA actions.

10. Security Considerations

Security considerations may appear to be orthogonal to green
networking considerations. However, there are a number of important
caveats.

Security vulnerabilities of networks may manifest themselves in
compromised energy efficiency. For example, attackers could aim at
increasing energy consumption to drive up attack victims' energy
bills. Specific vulnerabilities will depend on the particular
mechanisms. For example, in the case of monitoring energy
consumption data, tampering with such data might result in
compromised energy optimization control loops. Hence, any mechanisms
to instrument and monitor the network for such data need to be
properly secured to ensure authenticity.

In some cases, there are inherent trade-offs between security and
maximal energy efficiency that might otherwise be achieved. An
example is encryption, which requires additional computation for
encryption and decryption activities and security handshakes, in
addition to the need to send more traffic than necessitated by the
entropy of the actual data stream. Likewise, mechanisms that allow
to turn resources on or off could become a target for attackers.

Energy consumption can be used to create covert channels, which is a
security risk for information leakage. For instance, the temperature
of an element can be used to create a Thermal Covert Channel [TCC],
or the reading/sharing of the measured energy consumption can be
abused to create a covert channel (see for instance [DRAM] or
[NewClass]). Power information may be used to create side-channel
attacks. For instance, [SideChannel] provides a review of 20 years
of study on this topic. Any new parameters considered in protocol
designs or in measurements are susceptible to create such covert or
side channels, and this should be taken into account while designing
energy-efficient protocols.

11. Informative References

[Ahlgren2012survey]
Ahlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D.,
and B. Ohlman, "A survey of information-centric
networking", IEEE Communications Magazine, vol. 50, no. 7,
pp. 26-36, DOI 10.1109/MCOM.2012.6231276, 2012,
<https://doi.org/10.1109/MCOM.2012.6231276>.

[Alizadeh2010DCTCP]
Alizadeh, M., Greenberg, A., Maltz, D., Padhye, J., Patel,
P., Prabhakar, B., Sengupta, S., and M. Sridharan, "Data
Center TCP (DCTCP)", ACM SIGCOMM Computer Communication
Review, vol. 40, no. 4, pp. 63-74,
DOI 10.1145/1851275.1851192, 2010,
<https://doi.org/10.1145/1851275.1851192>.

[Bianco2016energy]
Bianco, A., Mashayekhi, R., and M. Meo, "Energy
consumption for data distribution in content delivery
networks", IEEE International Conference on Communications
(ICC), pp. 1-6, DOI 10.1109/ICC.2016.7511356, 2016,
<https://doi.org/10.1109/ICC.2016.7511356>.

[Bolla2011energy]
Bolla, R., Bruschi, R., Davoli, F., and F. Cucchietti,
"Energy Efficiency in the Future Internet: A Survey of
Existing Approaches and Trends in Energy-Aware Fixed
Network Infrastructures", IEEE Communications Surveys and
Tutorials, vol. 13, no. 2, pp. 223-244,
DOI 10.1109/SURV.2011.071410.00073, 2011,
<https://doi.org/10.1109/SURV.2011.071410.00073>.

[Cervero15]
Cervero, A. G., Chincoli, M., Dittmann, L., Fischer, A.,
Garcia, A., Galán-Jiménez, J., Lefevre, L., Meer, H. D.,
Monteil, T., Monti, P., Orgerie, A., Pau, L., Phillips,
C., Ricciardi, S., Sharrock, R., Stolf, P., Trinh, T., and
L. Valcarenghi, "Green Wired Networks", Large-Scale
Distributed Systems and Energy Efficiency, pp. 41-80,
DOI 10.1002/9781118981122.ch3, 2015,
<https://doi.org/10.1002/9781118981122.ch3>.

[Chabarek08]
Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang,
D., and S. Wright, "Power Awareness in Network Design and
Routing", IEEE INFOCOM 2008 - The 27th Conference on
Computer Communications, pp. 457-465,
DOI 10.1109/INFOCOM.2008.93, 2008,
<https://doi.org/10.1109/INFOCOM.2008.93>.

[DRAM] Paiva, T. B., Navaridas, J., and R. Terada, "Robust Covert
Channels Based on DRAM Power Consumption", Information
Security, ISC 2019, Lecture Notes in Computer Science, vol
11723, DOI 10.1007/978-3-030-30215-3_16, 2019,
<https://doi.org/10.1007/978-3-030-30215-3_16>.

[Emergy] Raghavan, B. and J. Ma, "The Energy and Emergy of the
Internet", Proceedings of the 10th ACM Workshop on Hot
Topics in Networks, no. 9, pp. 1-6,
DOI 10.1145/2070562.2070571, 2011,
<https://doi.org/10.1145/2070562.2070571>.

[Framework]
Faber, G., "A framework to estimate emissions from virtual
conferences", International Journal of Environmental
Studies, vol. 78, no. 4, pp. 608-623,
DOI 10.1080/00207233.2020.1864190, 2021,
<https://doi.org/10.1080/00207233.2020.1864190>.

[GreenNet22]
Clemm, A. and C. Westphal, "Challenges and Opportunities
in Green Networking", 1st International Workshop on
Network Energy Efficiency in the Softwarization Era, IEEE
NetSoft 2022, DOI 10.1109/NetSoft54395.2022.9844020, June
2022, <https://doi.org/10.1109/NetSoft54395.2022.9844020>.

[GREEN_METRICS]
Clemm, A., Ed., Pignataro, C., Ed., Schooler, E.,
Ciavaglia, L., Rezaki, A., Mirsky, G., and J. Tantsura,
"Green Networking Metrics for Environmentally Sustainable
Networking", Work in Progress, Internet-Draft, draft-cx-
green-green-metrics-00, 21 October 2024,
<https://datatracker.ietf.org/doc/html/draft-cx-green-
green-metrics-00>.

[Herzen2011PIE]
Herzen, J., Westphal, C., and P. Thiran, "Scalable routing
easy as PIE: A practical isometric embedding protocol",
19th IEEE International Conference on Network Protocols
(ICNP), pp. 49-58, DOI 10.1109/ICNP.2011.6089081, 2011,
<https://doi.org/10.1109/ICNP.2011.6089081>.

[Hossain2019]
Hossain, M., Georges, J., Rondeau, E., and T. Divoux,
"Energy, Carbon and Renewable Energy: Candidate Metrics
for Green-Aware Routing?", Sensors, vol. 19, no. 3,
DOI 10.3390/s19132901, June 2019,
<https://doi.org/10.3390/s19132901>.

[IETF-Net0]
Daley, J., "Towards a net zero IETF", IETF News, 6 May
2022,
<https://www.ietf.org/blog/towards-a-net-zero-ietf/>.

[Islam2012evaluating]
Islam, S. U. and J. Pierson, "Evaluating Energy
Consumption in CDN Servers", Proceedings of the Second
International Conference on ICT as Key Technology against
Global Warming, Lecture Notes in Computer Science, vol
7453, pp. 64-78, DOI 10.1007/978-3-642-32606-6_6, 2012,
<https://doi.org/10.1007/978-3-642-32606-6_6>.

[Junkyard] Switzer, J., Kastner, R., and P. Pannuto, "Architecture of
a Junkyard Datacenter", DOI 10.48550/arXiv.2110.06870,
arXiv: 2110.06870v1, 2021,
<https://arxiv.org/abs/2110.06870v1>.

[Krol2017NFaaS]
Krol, M. and I. Psaras, "NFaaS: Named Function as a
Service", ICN '17: Proceedings of the 4th ACM Conference
on Information-Centric Networking, pp. 134-144,
DOI 10.1145/3125719.3125727, 2017,
<https://doi.org/10.1145/3125719.3125727>.

[Mathew2011energy]
Mathew, V., Sitaraman, R., and P. Shenoy, "Energy-Aware
Load Balancing in Content Delivery Networks",
DOI 10.48550/arXiv.1109.5641, arXiv: 1109.5641v1, 2011,
<https://arxiv.org/abs/1109.5641>.

[NewClass] Khatamifard, S. K., Wang, L., Kose, S., and U. R.
Karpuzcu, "A New Class of Covert Channels Exploiting Power
Management Vulnerabilities", IEEE Computer Architecture
Letters, vol. 15, no. 2, pp. 201-204,
DOI 10.1109/LCA.2018.2860006, 2018,
<https://doi.org/10.1109/LCA.2018.2860006>.

[POWER_YANG]
Li, T. and R. Bonica, "A YANG model for Power Management",
Work in Progress, Internet-Draft, draft-li-green-power-00,
17 October 2024, <https://datatracker.ietf.org/doc/html/
draft-li-green-power-00>.

[QUAL] Li, R., Makhijani, K., Yousefi, H., Westphal, C., Xong,
L., Wauters, T., and F. D. Turck, "A Framework for
Qualitative Communications using Big Packet Protocol",
NEAT'19: Proceedings of the ACM Sigcomm Workshop On
Networking For Emerging Applications And Technologies, pp.
22-28, DOI 10.1145/3341558.3342201, 2019,
<https://doi.org/10.1145/3341558.3342201>.

[Ren2018jordan]
Ren, J., Lu, K., Westphal, C., Wang, J., Wang, J., Song,
T., Liu, S., and J. Wang, "JORDAN: A Novel Traffic
Engineering Algorithm for Dynamic Adaptive Streaming over
HTTP", 2018 International Conference on Computing,
Networking and Communications (ICNC), pp. 581-587,
DOI 10.1109/ICCNC.2018.8390337, 2018,
<https://doi.org/10.1109/ICCNC.2018.8390337>.

[RFC2481] Ramakrishnan, K. and S. Floyd, "A Proposal to add Explicit
Congestion Notification (ECN) to IP", RFC 2481,
DOI 10.17487/RFC2481, January 1999,
<https://www.rfc-editor.org/info/rfc2481>.

[RFC3031] Rosen, E., Viswanathan, A., and R. Callon, "Multiprotocol
Label Switching Architecture", RFC 3031,
DOI 10.17487/RFC3031, January 2001,
<https://www.rfc-editor.org/info/rfc3031>.

[RFC3095] Bormann, C., Burmeister, C., Degermark, M., Fukushima, H.,
Hannu, H., Jonsson, L., Hakenberg, R., Koren, T., Le, K.,
Liu, Z., Martensson, A., Miyazaki, A., Svanbro, K.,
Wiebke, T., Yoshimura, T., and H. Zheng, "RObust Header
Compression (ROHC): Framework and four profiles: RTP, UDP,
ESP, and uncompressed", RFC 3095, DOI 10.17487/RFC3095,
July 2001, <https://www.rfc-editor.org/info/rfc3095>.

[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, DOI 10.17487/RFC3168, September 2001,
<https://www.rfc-editor.org/info/rfc3168>.

[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.

[RFC8402] Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L.,
Decraene, B., Litkowski, S., and R. Shakir, "Segment
Routing Architecture", RFC 8402, DOI 10.17487/RFC8402,
July 2018, <https://www.rfc-editor.org/info/rfc8402>.

[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/info/rfc9000>.

[RFC9330] Briscoe, B., Ed., De Schepper, K., Bagnulo, M., and G.
White, "Low Latency, Low Loss, and Scalable Throughput
(L4S) Internet Service: Architecture", RFC 9330,
DOI 10.17487/RFC9330, January 2023,
<https://www.rfc-editor.org/info/rfc9330>.

[Shannon] Shannon, C. E., "A Mathematical Theory of Communication",
The Bell System Technical Journal, vol. 27, no. 3, pp.
379-423, DOI 10.1002/j.1538-7305.1948.tb01338.x, July
1948,
<https://doi.org/10.1002/j.1538-7305.1948.tb01338.x>.

[SideChannel]
Randolph, M. and W. Diehl, "Power Side-Channel Attack
Analysis: A Review of 20 Years of Study for the Layman",
Cryptography, vol. 4, no. 2,
DOI 10.3390/cryptography4020015, 2020,
<https://doi.org/10.3390/cryptography4020015>.

[TCC] Rahimi, P., Singh, A. K., and X. Wang, "Selective Noise
Based Power-Efficient and Effective Countermeasure Against
Thermal Covert Channel Attacks in Multi-Core Systems",
Journal on Low Power Electronics and Applications, vol.
12, no. 2, DOI 10.3390/jlpea12020025, 2022,
<https://doi.org/10.3390/jlpea12020025>.

[Telefonica2021]
Telefonica, "Telefonica Consolidated Annual Report 2021",
2021.

[TradeOff] Welzl, M., "Not a Trade-Off: On the Wi-Fi Energy
Efficiency of Effective Internet Congestion Control", 2022
17th Wireless On-Demand Network Systems and Services
Conference (WONS), pp. 1-4,
DOI 10.23919/WONS54113.2022.9764413, 2022,
<https://doi.org/10.23919/WONS54113.2022.9764413>.

[TVR_REQS] King, D., Contreras, L. M., Sipos, B., and L. Zhang, "TVR
(Time-Variant Routing) Requirements", Work in Progress,
Internet-Draft, draft-ietf-tvr-requirements-06, 7 July
2025, <https://datatracker.ietf.org/doc/html/draft-ietf-
tvr-requirements-06>.

[Westphal2021qualitative]
Westphal, C., He, D., Makhijani, K., and R. Li,
"Qualitative Communications for Augmented Reality and
Virtual Reality", 22nd IEEE International Conference on
High Performance Switching and Routing (HPSR), pp. 1-6,
DOI 10.1109/HPSR52026.2021.9481793, 2021,
<https://doi.org/10.1109/HPSR52026.2021.9481793>.

[Wolf2014choicenet]
Tilman, W., Griffioen, J., Calvert, L., Dutta, R.,
Rouskas, G., Baldin, I., and A. Nagurney, "ChoiceNet:
Toward an Economy Plane for the Internet", ACM SIGCOMM
Computer Communciations Review, vol. 44, no. 3, pp. 58-65,
DOI 10.1145/2656877.2656886, July 2014,
<https://doi.org/10.1145/2656877.2656886>.

Acknowledgments

The authors thank Dave Oran for providing the information regarding
covert channels using energy measurements and Mike King for an
exceptionally thorough review and useful comments.

Contributors

Michael Welzl
University of Oslo
Email: michawe@ifi.uio.no

Authors' Addresses

Alexander Clemm (editor)
Independent
Los Gatos, CA
United States of America
Email: ludwig@clemm.org

Carlos Pignataro (editor)
North Carolina State University & Blue Fern
United States of America
Email: cpignata@gmail.com, cmpignat@ncsu.edu cmpignat@ncsu.edu, carlos@bluefern.consulting

Cedric Westphal
Email: westphal@ieee.org

Laurent Ciavaglia
Nokia
Email: laurent.ciavaglia@nokia.com

Jeff Tantsura
Nvidia
Email: jefftant.ietf@gmail.com

Marie-Paule Odini
Email: mp.odini@orange.fr