



Internet Research Task Force (IRTF)                        A. Clemm, Ed.
Request for Comments: 9845                                     Sympotech
Category: Informational                                C. Pignataro, Ed.
ISSN: 2070-1721               NC State University & Blue Fern Consulting
                                                             C. Westphal
                                    University of California, Santa Cruz
                                                            L. Ciavaglia
                                                                   Nokia
                                                             J. Tantsura
                                                                  Nvidia
                                                              M-P. Odini
                                                            October 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.  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, 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.

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   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
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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 carbon dioxide (CO2).
   CO2 is emitted in the process of burning fuels to generate energy
   that is used, for example, to power electrical devices such as
   networking equipment.  Those fuels often include fossil fuels (such
   as oil), which releases CO2 that had 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.
   Other GHGs such as CH4 and N2O are associated with electricity
   generation as well.  Although they are emitted in smaller quantities,
   they have an even higher Global Warming Potential (GWP).  To
   facilitate accounting for them, they are collectively simply
   converted into CO2 equivalents (CO2e).

   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 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 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.  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, not just by enabling others to reduce
   their reliance on energy but also by 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 2024,
   its network's energy consumption per petabyte (PB) of data amounted
   to 38 megawatt-hours (MWh) [Telefonica2024].  This represents an
   improvement of about 90% since the 2015 base year (400 MWh/PB),
   achieved through steady year-on-year efficiency gains, although these
   are still partly offset by simultaneous growth in data volume.  The
   same report highlights an important corporate goal: continuing on
   this trajectory and further reducing overall greenhouse gas
   emissions.

1.2.  Approaching the Problem

   One way in which gains in network sustainability can be achieved
   involves reducing the amount of energy needed to provide
   communication services and improving the efficiency with which
   networks utilize power during their use 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 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.

   Network management has an outsized role to play in approaching those
   problems.  To reduce the amount of energy used, network providers
   need to maximize the use of scarce resources and eliminate the use of
   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 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; improving energy efficiency and reducing
         energy consumption during operations; and increasing reuse,
         repurposing, and 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 requires accompanying
      management functionality, for example to determine when it is
      "safe" to down-speed a link or enter a power saving mode, and
      operate the network in such a way that 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" 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 less energy-efficient, or be
      powered less-sustainable energy 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

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

   SDN:  Software-Defined Networking

   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 placed on technology that enables,
   for example, very efficient and rapid discovery, monitoring, and
   control of networking resources.  This allows devices to be
   dynamically taken offline or brought back online without extensive
   network-level state convergence, route recalculation, or other
   complex optimizations at the network level.  To facilitate such
   schemes, rapid power cycle and initialization 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 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, 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 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 allow for the energy 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 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: 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
   [Shannon].  Currently, most of the traffic over the Internet consists
   of video streaming.  Video encoders are already quite efficient and
   keep improving all the time.  This results in energy savings as one
   of many benefits, even if some of those savings 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 [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; this includes advances such as coding
      improvements, reductions in header tax, lower protocol verbosity,
      and reduced need for 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 needing to be maintained and
   synchronized.  Memory as well as the processing needed to maintain
   and synchronize state both consume energy.  Exploring ways to reduce
   the amount of memory and synchronization of state that is required
   offers opportunities 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 (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 long 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 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 to smartly place functionality in
   the network for optimal effectiveness.  An example is placement of
   virtualized network functions in carbon-optimized ways.  For example,
   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 resources from traffic is an important
   consideration for energy-efficient traffic steering.  This approach
   contrasts and indeed conflicts with existing schemes that typically
   aim to create redundancy and load-balance traffic across a network to
   achieve even resource utilization across larger numbers of network
   resources as a means to increase network resilience, optimize service
   levels, and ensure fairness.  Thus, a big challenge is how 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 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 large
   carbon footprint?

   Similarly, how can the carbon cost of a flow be assessed?  That might
   serve many purposes beyond network optimization, from the option to
   introduce green billing and charging 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.

   As part of adding carbon and energy awareness into networks, it is
   useful also for topology information to provide visibility into
   sustainability data.  Such capabilities can help to assess
   sustainability of the network overall and 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 Smart NICs 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, most suitable
      placement of computational intelligence and network functionality
      within the network design, industrial operations and control,
      massively distributed ML and AI) to optimize overall
      sustainability and holistic approaches to trade off carbon
      footprint 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 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 (but not limited to) the following:

   *  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 the energy cost needed to transmit data to a Low Earth Orbit
      (LEO) satellite constellation offset by the fact that the
      constellation and any networking 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.  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://hal.science/hal-02169758/>.

   [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>.

   [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>.

   [Telefonica2024]
              Telefonica, "Telefonica Consolidated Annual Report 2024",
              2025, <https://www.telefonica.com/en/wp-
              content/uploads/sites/5/2025/02/Consolidated-Annual-
              Accounts-2024.pdf>.

   [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 Communications 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)
   Sympotech
   Los Gatos, CA
   United States of America
   Email: ludwig@clemm.org


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


   Cedric Westphal
   Department of Computer Science and Engineering
   University of California, Santa Cruz
   Email: cedric@ucsc.edu


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


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


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