



Network Working Group                                           M. Welzl
Internet-Draft                                        University of Oslo
Intended status: Informational                                E. Stephan
Expires: 8 January 2026                                           Orange
                                                             E. Schooler
                                                    University of Oxford
                                                               S. Rumley
                                                                  HES-SO
                                                               A. Rezaki
                                                                   Nokia
                                                               J. Manner
                                                        Aalto University
                                                            C. Pignataro
                                                    Blue Fern Consulting
                                                              M. Palmero
                                                                   Cisco
                                                             J. Lindblad
                                                             All For Eco
                                                             S. Krishnan
                                                                   Cisco
                                                              A. Keränen
                                                                Ericsson
                                                            H. ElBakoury
                                                                        
                                                         L. M. Contreras
                                                              Telefonica
                                                                A. Clemm
                                                             Independent
                                                                J. Arkko
                                                                Ericsson
                                                             7 July 2025


 Architectural Considerations for Environmentally Sustainable Internet
                               Technology
              draft-various-eimpact-arch-considerations-01

Abstract

   This document discusses protocol and network architecture aspects
   that may have an impact on the sustainability of network technology.
   The focus is on providing guidelines that can be helpful for protocol
   designers and network architects, where such guidelines can be given.

About This Document

   This note is to be removed before publishing as an RFC.




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   The latest revision of this draft can be found at
   https://jariarkko.github.io/draft-eimpact-arch-considerations/draft-
   eimpact-arch-considerations.html.  Status information for this
   document may be found at https://datatracker.ietf.org/doc/draft-
   various-eimpact-arch-considerations/.

   Source for this draft and an issue tracker can be found at
   https://github.com/jariarkko/draft-eimpact-arch-considerations.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on 8 January 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Understanding . . . . . . . . . . . . . . . . . . . . . . . .   7
     2.1.  Measurement and Modeling  . . . . . . . . . . . . . . . .   7
       2.1.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .   7
       2.1.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .   8
       2.1.3.  Recommendation  . . . . . . . . . . . . . . . . . . .   9



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   3.  Actions . . . . . . . . . . . . . . . . . . . . . . . . . . .  12
     3.1.  Dynamic Scaling . . . . . . . . . . . . . . . . . . . . .  12
       3.1.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .  12
       3.1.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  14
       3.1.3.  Recommendation  . . . . . . . . . . . . . . . . . . .  16
     3.2.  Transport . . . . . . . . . . . . . . . . . . . . . . . .  16
       3.2.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .  16
       3.2.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  17
       3.2.3.  Recommendation  . . . . . . . . . . . . . . . . . . .  18
     3.3.  Equipment Longevity . . . . . . . . . . . . . . . . . . .  19
       3.3.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .  19
       3.3.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  20
       3.3.3.  Recommendation  . . . . . . . . . . . . . . . . . . .  21
     3.4.  Encoding  . . . . . . . . . . . . . . . . . . . . . . . .  21
       3.4.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .  22
       3.4.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  22
       3.4.3.  Recommendation  . . . . . . . . . . . . . . . . . . .  22
     3.5.  Sustainable by Design: Data Governance Perspective  . . .  23
       3.5.1.  Motivation  . . . . . . . . . . . . . . . . . . . . .  23
       3.5.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  23
       3.5.3.  Recommendation  . . . . . . . . . . . . . . . . . . .  23
   4.  Recommendations for Protocol Design . . . . . . . . . . . . .  24
   5.  Recommendations for Further Work and Research . . . . . . . .  25
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  25
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  26
   8.  Informative References  . . . . . . . . . . . . . . . . . . .  26
   Appendix A.  Modeling Approaches and Literature . . . . . . . . .  29
     A.1.  Customer Attribution  . . . . . . . . . . . . . . . . . .  30
     A.2.  Baselining and Benchmarking . . . . . . . . . . . . . . .  30
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  31
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  31

1.  Introduction

   This document discusses protocol and network architecture aspects
   that can have an impact on the environmental sustainability of
   network technology.  For brevity, we will use the term sustainability
   in this document to refer to environmental sustainability.  We do
   note that sustainability as a term is widely used to refer to
   different notions of sustainability, and the most well-known larger
   definition of sustainability can be seen from the United Nations
   Sustainable Development Goals (UN SDG) [UNSDG].









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   Environmental sustainability is an important consideration in
   society, and in networking, too.  Networking technologies enable
   societies to operate in an environmentally sustainable manner and
   thereby have a positive handprint, yet networks themselves must be
   environmentally sustainable and attempt to minimise their negative
   footprint.

   Fundamentally the question we try to address concerns the resource
   usage and the lifecycle of network equipment.  The less devices are
   built, and energy is used, the less emissions are created.  Networks
   are built with hardware and these in turn use electrical energy to
   run.  Eventually, the hardware is decommissioned and some amount of
   the materials are recycled.

   We can divide the lifecycle into three major phases (omitting some
   intermittent steps like shipping of products):

   1.  Manufacturing (including the raw material extraction and usage,
       the embedded chips and electronics, casing, and energy needed for
       these operations, etc.),

   2.  Use phase that is focused on the operational energy use and
       repairing equipment, and

   3.  End of life that can include both direct recycling of some of the
       materials or finding a new life and usage for an old product that
       still functions, after which it is finally recycled.

   Networks also require some amount of physical construction to
   realize, and this construction work also creates emissions.  This
   category of emissions is out of scope of this document because the
   Internet community and network engineers have limited means to impact
   construction work itself and the associated industry, but we can
   impact how networks, protocols and hardware are designed, built and
   operated.

   All these phases create harmful emissions, into the ground and in the
   air, that have a negative impact on our environment and people.  As
   the type of such emissions vary, they are often standardized as
   carbon dioxide equivalent (CO2e) to allow comparing sources and
   amounts of emissions.  When discussing (carbon) emissions in this
   document, we generally refer to CO2e.

   The manufacturing of networking hardware, both for fixed and wireless
   networks, is a significant source of emissions, and recycling of ICT
   equipment is still limited to the casing and some other minor parts.
   Direct energy usage of networking and the source of the energy have
   often been the primary concerns.  There are many reports and



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   scientific papers discussing carbon emissions of the energy used by
   ICT.  As of today, and the foreseeable future, the difference in
   emissions of the electric grid between countries and regions can vary
   significantly. e.g. In the EU, there are 10-fold differences between
   countries, and similar differences exist between US states.  On a
   global level, the differences can be over 50-fold.  Yet, as the world
   moves towards greener energy production, the relative negative
   impacts related to manufacturing becomes more prominent and the
   importance of equipment longevity grows.

   When good design and architecture can improve the sustainability of
   networks, they should certainly be applied to designing new protocols
   and building networks.  Intuitively, protocol and network
   architecture choices can have an impact on sustainability.  At the
   very least the right design and architecture can make it possible to
   have a positive impact, but of course the architecture alone is not
   enough.  The possibilities offered by the architecture need to be
   realized by implementations and practical deployments.

   To give an example of an architectural aspect that potentially has a
   sustainability impact, enabling the collection of information (e.g.,
   energy consumption) and then using that information to make smarter
   decisions is one.  For instance, understanding power consumption of
   individual nodes can be valuable input to future purchasing decisions
   or development efforts to reduce the power consumption.  Yet, as data
   collection is often rather easy, it is easy to overdo it in such a
   way that it leads to accumulation of dark data (i.e. data that is
   collected and stored but never used).  All data collection consumes
   processing power, network resources and storage space, and this can
   in turn increase the emissions from the network.

   Other architectural examples include making it possible to scale
   resources or resource selection processes performed in a
   sustainability-aware fashion.  The use of communication primitives
   that maximize utility in a given problem (e.g., using multicast) or
   the use of technologies that reduce the number or size of messages
   needed for a given task (e.g., binary encoding instead of textual)
   are some further examples.

   Of course, some of these aspects may have a major impact on
   sustainability, where others may only have a minor effect.  There are
   also tradeoffs, such as side-effects of architectural choices, e.g.,
   dynamic scaling of a router network potentially impacts jitter;
   putting cellular base stations to sleep and activating them as
   capacity needs grow potentially introduces a delay in matching the
   needs of the data flows.





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   The document is intended to help engineering efforts in the IETF,
   provide operational guidance in the operator community, as well as to
   point to potential research directions in the IRTF.

   The scope of the document is advice on Internet and protocol
   architecture, such as what architecture or capabilities new protocol
   designs or features should have, what kind of operational network
   architectures should be deployed, and how all of these can be
   designed to best address sustainability concerns.

   The focus of this document is to provide actionable design advice to
   protocol designers.  This document therefore addresses one aspect in
   the architecture question and does not claim to cover the topic
   exhaustively.

   This document is not focused on general issues around environmental
   sustainability, except those that pertain to architecture or
   significant protocol features.

   It is to be noted that networks themselves are a service, a tool, for
   all the applications and services on the Internet.  Networks connect
   data, people and services.  The increase in networking and size of
   the Internet is driven by these applications and the usage.
   Therefore, the emissions from networking are tied to the applications
   and the data they consume; with less applications or data, the
   Internet would have less hardware and less energy usage.  The goals
   of this document are not to instruct application and service
   developers to choose what applications are worthwhile or how much
   content is sent.  There are many forums and parties whose mission is
   to help these developers to implement more sustainable services, such
   as, the Green Software Foundation, the Green Web Foundation, Greening
   of Streaming, to name a few.

   The next two sections present architectural and protocol design
   aspects that can have an impact on the sustainability of networking.
   Section 2 discusses those foundations that are required to prepare
   for sustainability improvements, and Section 3 discusses actions that
   can be taken to make the actual improvements.  For each topic in
   these sections, we provide an overview, the motivation for why it
   would be important to consider for more sustainable networking, an
   analysis and recommendations for future networking professionals.

   Recommendations for protocol designers are discussed throughout the
   document and summarized in Section 4.  Finally, Section 5 discusses
   further work that is needed to make further concrete recommendations
   for the designers.





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2.  Understanding

   It is essential to understand the current state of affairs before any
   improvements can be made.  Thus, some level of measurement is
   necessary for starting to improve sustainability.  In many cases
   measurements are also complemented by modeling.  In some cases
   modeling needs to be used since direct measurements may not always be
   available.  Modeling is also used to combine measurements in ways
   that make it more effective in aiding the understanding of the
   effects of the potential actions. e.g. Modeling could be used to play
   out multiple what-if scenarios based on the actions recommended in
   Section 3.

2.1.  Measurement and Modeling

   The key goals of measuring and modeling are to identify potential
   areas of improvement, and to establish a baseline to benchmark any
   improvements that are effected by the performed actions.  This not
   only helps defining an objective data-driven approach to improvement,
   but also can illustrate what actions can cause a bigger impact.  This
   could help prioritize what actions can be taken and when.  This draft
   assumes that the specific semantics of sustainability-related
   measurements (e.g., carbon factors, device-specific formulas) are
   defined elsewhere and focuses instead on enabling architectures to
   support measurement, collection, and use.

2.1.1.  Motivation

   Without measurements of any kind, it is impossible to assess if the
   networks are functioning correctly.  It is impossible to know if the
   system is efficient by comparing it against a baseline model.  It is
   also impossible to check that changes aiming at optimizing something
   are indeed valuable.

   This is particularly the case when looking at the systems as a whole
   in post-analysis.  As discussed earlier, some level of measurements
   is useful input for further actions, such as deciding what parts of
   the network need to be targeted for further improvement.

   But measurements may also be useful for some dynamic situations where
   power-saving decisions, for instance, depend on knowing the relative
   power consumption of different activities, such as when a power-off
   decision involves understanding the relative savings during the
   shutdown period vs. the power cost of shutdown and startup
   procedures, or the possible need to reconfigure other nodes in the
   network due to the shutdown.





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   At the same time, it is not possible (or even desirable) to measure
   everything.  Excessive measurement collection without clear
   objectives can have a negative impact by itself and some
   considerations in this regard can be found in Section 3.5
   Furthermore, any measurement must be validated.  Relevance of
   measurements must be periodically assessed, e.g., with comparisons
   between measurements within a network and the aggregate numbers from
   the electricity provider.

   Finally, measurements made in the field must be collected and
   structured to allow later retrieval.  And measurements are
   counterproductive if they are endlessly accumulated without being
   consulted.

2.1.2.  Analysis

   This section discusses how measurements relate to the fabrication and
   usage phases and how efficiency can be measured.

   While power consumption is the most commonly used sustainability
   metric, this document does not attempt to define energy metrics or
   modeling standards.  Those topics are in scope for the GREEN WG
   (focused on operational energy) and the SUSTAIN RG (which addresses
   broader life-cycle impacts and carbon modeling).  This section
   focuses on the architectural implications of enabling measurement,
   not metric definitions.

2.1.2.1.  Measuring impacts of fabrication phase

   Network infrastructure generates negative impacts principally during
   fabrication and usage phases.  Measuring negative impacts related to
   fabrication falls in the activity of lifecycle analysis (LCA).  LCAs
   are typically performed per device, either by the equipment vendor
   itself, or by third-party analysts.  LCA involves modeling (see
   Section 2.1.3.6).  The analysis can be done in terms of climate
   change (CC) but can be extended to other criteria as abiotic resource
   depletion (ARD), ecotoxicity (ET) or water usage (WU).  LCA also
   involves information systems keeping an inventory of the devices
   uses.  For many classes of devices, the embedded carbon aspects or
   use of raw materials are significant sustainability issues.  As these
   measurements and inventories are external to the network
   architecture, they are considered out of this document scope.









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2.1.2.2.  Measuring impacts of usage phase

   Measuring negative impacts related to the usage phase falls in the
   scope of monitoring.  In this phase, the most obvious sustainability-
   related measurement is power monitoring to measure the energy
   consumption and estimate the related negative impacts.

   Power (in Watts, that is, in Joule/s) or energy (in Joules)
   measurements alone are of meager use if the cause of the consumption
   is not measured as well.  Any power/energy measurement should occur
   alongside other measurements that can be used to determine energy
   efficiency.  Hence a sound measurement architecture implies the
   existence of an energy efficiency framework of some kind.

2.1.2.3.  Measuring efficiency

   In the context of carbon accounting, emission accountants are
   generally looking for a metric of the delivered value per unit of
   carbon.  In networking, the most obvious delivered value is number of
   bits sent or received (traffic), or to the communication capacity
   made available during unit of time.  In both cases, the unit is the
   bit, but the two metrics have very different meanings.  In one case,
   one spends a Joule to send a bit.  In the other case, one spends a
   Joule to offer a bandwidth capacity of 1 bit/s during a second.  The
   latter is important, as often communication networks have
   requirements to be able to send messages when there's a need for it,
   e.g., for emergency communications, even when those messages may not
   always be sent.

   The measurement of efficiency is not restricted to energy.  Traffic
   or offered bandwidth can be related to the carbon emitted by the
   device traversed by this traffic.  This carbon should include the
   part associated with electricity, but also the part associated with
   fabricating the device (pro rata temporis) [LCAandUsage].
   Sustainable efficiency can also be expressed in water used per
   traffic, for example.

2.1.3.  Recommendation

   The GREEN WG is chartered to define energy consumption metrics and
   associated frameworks.  The GREEN framework provides a foundational
   building blocks for monitoring and optimizing energy consumption
   across networked devices and components.








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   The SUSTAIN RG addresses broader measurement questions such as
   embedded emissions, raw materials, and life-cycle modeling.  This
   document assumes these efforts will define and validate the metrics
   themselves.  Our focus is on ensuring that Internet architecture
   enables effective collection, transport, and use of such metrics for
   operational decisions and reduction of environmental impacts.

   Aligning these efforts will support the development of composite
   metrics that connect operational energy use along with manufacturing/
   end-of-life considerations in order to establish a coherent basis for
   sustainable digital infrastructure management.

   In order to meet the needs discussed above, the following
   architectural design principles are proposed.

2.1.3.1.  Future Proof Metrics

   We recommend that any measurement framework or sustainability-related
   information sharing mechanism be designed to share different types of
   information and not limited to a single metric such as power
   consumption.  Requirements, units, granularity and collection method
   specifications are sure to shift over time.

2.1.3.2.  Plug-in Architecture for Collection and Control

   Since the need to deliver on the use cases described is urgent, the
   industry has to accommodate the capabilities (and limitations) of
   existing equipment in the field for collecting metrics.  It is
   recommended to apply a plug-in architecture with modules that can
   work with (read from and control) devices of any kind, including
   traditional networking hardware devices, cooling systems, software
   stacks, and occasionally static data sheets.

2.1.3.3.  Data with Content Declaration

   To make sense of the collected data, it must be possible to see
   exactly where all data is coming from, what it means, its precision
   and how it has been processed.  The metadata itself must also have a
   formal description.  YANG might be suitable for modeling the metadata
   schema.  Keep the metadata attached to the dataflow it describes, so
   that the relation is clear even when components are added by other
   organizations at a later point in time.









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2.1.3.4.  Processing Flexibility and Audit Trails

   The collected data passes through a pipeline from collection to
   decisions.  By processing we mean steps to reshape the data to match
   further aggregation and processing steps, such as unit conversions,
   sample frequency alignment, filtering, etc.

   Separate these pipeline stages into separate modules and use
   configuration to construct the pipeline.  This gives flexibility,
   reuse and enables a full audit trail.  It is essential that every
   data processing step can be reviewed in an audit situation without
   looking at code.

2.1.3.5.  Aligned with Reporting Frameworks

   Ensure that the system output is aligned with the measurement
   requirements set forth by relevant legal frameworks, e.g. ESRS
   (Europe), TCFD and IFRS (US, Japan), BRSR (India), etc.  The
   responsible corporate bodies producing the corporate reports are
   unlikely to use any technical collection system that isn't well
   aligned.

2.1.3.6.  Modeling

   Where power optimization choices are made, accurate information is
   required to decide the right choice.

   The paucity of up-to-date information on equipment and system
   parameters, especially power consumption and maximum throughput,
   makes estimating the power consumption and energy efficiency of these
   systems extremely challenging.  In addition, the rapid evolution of
   technology and products in ICT makes the estimation quickly outdated
   and possibly inaccurate.  In some cases, physical measurements have
   to be replaced by partial measurements and mathematical modeling.

2.1.3.6.1.  Power modeling

   To date, two approaches to network power modeling are accepted as
   providing a realistic estimate of network power consumption.  These
   approaches are referred to as "bottom-up" and "top-down".  The paper
   [Unifying] surveys both approaches and provide a new approach which
   unifies both of them.  The unified approach is used to estimate the
   power consumption of access, aggregation and core networks.








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   Modeling can also help address attribution aspects, such as those
   involved in an effort of an organization to calculate its Scope 3
   emissions.  Modeling can also be used to assist in establishing a
   baseline energy consumption, and enable later comparisons to that
   baseline.

   Additional discussion of modeling can be found in Appendix A.

3.  Actions

3.1.  Dynamic Scaling

   Dynamic scaling is the ability to adjust resources according to
   demand and possibly turn some of them off during periods of low
   usage.  Examples include the set of servers needed for a service, how
   many duplicate links are needed to carry high-volume traffic, whether
   one needs all base stations with overlapping coverage areas to be on,
   etc.

   Networks and communications are also critical functions of the modern
   digital society.  The reliability of individual networking links or
   devices cannot always be guaranteed.  As a result, various levels and
   forms of resiliency are often needed, for instance through
   redundancy.  Yet, there is a question on how much redundancy is
   needed and how quickly a backup or resource increase can be activated
   due to increased demand.

   Scaling can be pulled up and down by data consumption variations and
   more rarely by power shortage.  In such situation dynamic scaling is
   the ability to adjust demand resources according to resources.  When
   operating on limited backup energy sources such as batteries or
   generators, the architecture must support graceful adaptation before
   power runs out.  In such situations, networks must minimize
   consumption to extend operational time.

3.1.1.  Motivation

   Outside of implementation improvements, dynamic scaling is
   potentially the most promising method for reducing power consumption
   related environmental impacts.  Scaling can happen on a device-level
   (increasing performance as traffic levels grow) or a network segment
   level (increasing the number of active links or cellular base
   stations).

   Considering current fixed networking hardware, dynamic scaling might
   not have an impact in situations where there's only a single router
   or server serving a particular route, area, or function.  Current
   routers and switches exhibit limited potential dynamic scaling



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   because the focus is on high performance and a stable connectivity.
   There have been some recent improvements on this front as well. e.g.
   Energy-Efficient Ethernet (EEE) is a good example of a networking-
   level specification to lower energy consumption in idle mode.  EEE
   has limited impact on a network that has continuous traffic.

   Resiliency can be implemented within a single router as well, e.g. as
   a backup power supply, between routers and switches as multiple links
   between the same nodes, having different links between two end
   points, overlapping cellular coverage, etc.  All these necessarily
   add more hardware to provide the same exact service.  Some of that
   hardware can be fully operational at all times and used to serve the
   traffic, while other links may be in hot or cold standby depending on
   the use case.

   Cellular networks are typically built with significant overlap in
   coverage areas of multiple base stations.  Demand and business
   reasons dictate the design of the coverage, and regulations might
   dictate how reliable the cellular service should be.  There is
   extensive work world-wide to optimize the operation of this
   overlapping coverage, e.g. by turning down some sites at night time
   when traffic volumes are low.  A cellular base station site can
   consume anything from a few kWh to ten or more kWh per provider.
   Modern cellular base stations do implement numerous features to scale
   the energy consumption.  In general, cellular base stations have a
   base energy consumption and traffic-dependent consumption, a somewhat
   similar behavior to what we can observe in modern CPUs.

   On the network level, most large systems have significant amount of
   redundancy and spare capacity.  Where such capacity can be turned on
   or off to match the actual need at a given time, significant
   reductions in power consumption can be achieved.

   Whereas scaling down under normal conditions seeks to reduce
   consumption while maintaining full capabilities, power-constrained
   operations accept degraded performance or functionality.  Operating
   in power backup mode introduces a shift in network behavior as it
   differs from network-driven auto scaling:

   *  Network, devices and components must reduce power usage, possibly
      sacrificing performance, feature sets, or redundancy.

   *  Each network domain (RAN, edge, and core network segments) faces
      its own constraints and policies in power-limited operation.







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3.1.2.  Analysis

   Dynamic scaling could be seen as either an alternative or
   complementary to load stabilization, e.g., via "peak shaving".
   Perhaps the most realistic view is that both are likely needed.

   The most rudimentary approach to dynamic scaling is just turning some
   resources off.  However this may not be sufficient, and a more
   graceful/engineered approach potentially yields better results.

   Network architects need to understand the impacts of scaling changes
   on users and traffic.  These may include the fate of ongoing
   sessions, latency/jitter, packets in flight, or running processes,
   attempts to contact resources that are no longer present, and the
   time it takes for the network to converge to its new state.

   Dynamic scaling requires an understanding of load levels for the
   network, so information collection is required.  It also requires
   understanding the power, time and other costs of making changes.
   (See [I-D.pignataro-enviro-sustainability-architecture] for
   discussion of tradeoffs and multi-objective optimization.)

   Understanding the resiliency requirements for a network or a piece of
   equipment is also important for the optimal control of resiliency,
   e.g., as an input to decisions on how many instances of replicated
   services need to be run and where.

   Some of the strategies that are useful in implementing effective
   dynamic scaling include:

   *  Matching the currently used resources to the actual need, be it
      about traffic demand or resiliency.  One way to do this is to use
      power-proportional underlying technologies, such as chipsets or
      transmission technologies.  And where this is not sufficient, the
      ability to turn components/systems on and off is an alternative
      strategy.

   *  Using load adaptive techniques allows the capacity of the nodes to
      be dynamically adjusted according to the demand.  Examples include
      Adaptive Link Rate (ALR), which dynamically adapts the link rate
      to suit traffic demand or power off links in Link Aggregation
      based on traffic demand which is empirically estimated based on
      traffic arrival.  LACP (Link Aggregation Control Protocol) defined
      in IEEE 802.1AX [LinkAggregation] can be modified to power off
      links in an aggregation if they are not needed.

   *  Ability to enter "no new work" mode for equipment, to enable some
      resources to be eventually released/turned off.



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   *  Ability to move ongoing tasks off to other equipment, to prevent
      disruption of already started tasks.

   *  Ability to schedule changes in advance rather than making them
      abruptly, with associated signaling exchanges and possible
      transient routing and other failures.  See for instance the time-
      variant routing work in the IETF [RFC9657]
      [I-D.ietf-tvr-requirements] [I-D.ietf-tvr-schedule-yang]
      [I-D.ietf-tvr-alto-exposure].

   *  Efficient propagation of changes of new routes, new set of
      servers, etc. in order to reduce the amount of time where state is
      not synchronized across the network.  The needs for the
      propagation solution needs to be driven by dynamic scaling and
      sustainability as well as other aspects, such as recovery from
      failures.

   *  Build mechanisms to deal with dynamic changes: Plan for dynamic
      set of resources and not expect to work with a fixed set of
      resources.

   *  Dynamic scaling requires automation in most cases, e.g., to turn
      on new service instances.  See again
      [I-D.pignataro-enviro-sustainability-architecture] for a
      discussion of automation.

   *  Interaction with the energy grid can enable dynamic load shifting.
      For instance, a demand-response technique can be used where the
      system temporarily reduces its energy usage in response to pricing
      signals from a smart grid.  The proposed demand-response technique
      involves deferring the load from elastic requests to later time
      periods in order to reduce the server demand and the current
      energy usage, and hence, energy costs [LoadShifting].

   *  Energy-aware routing.  This generally aims at aggregating traffic
      flows over a subset of the network devices and links, allowing
      other links and interconnection devices to be switched off.  These
      solutions shall preserve connectivity and QoS, for instance by
      limiting the maximum utilization over any link or ensuring a
      minimum level of path diversity.  There are also algorithms for
      Green Traffic engineering.  For instance, [Segment] employs
      segment routing.  Experimental analysis results [Experiment] show
      that the resource usage for SRv6 could be more than 70% lower than
      that of the SPF-based forwarding, depending on the network
      topology.






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3.1.3.  Recommendation

   The guidelines above need to be considered specifically for each
   protocol and system design.  Further work in detailing this guidance
   would also be useful.

   It is likely that there is increased attention to resiliency in the
   future, given for instance the increased importance of the tasks
   supported by networks or the potentially increasing frequency of
   natural disasters as a result of global warming.

   Scaling steps during power shortage differ from network dynamic
   scaling and depend on the network domain and the events: grid
   outages, deployment in remote or mobile environments, extreme weather
   events, or any sort of enforced reductions in power usage like
   monthly battery testing.  Nevertheless, there is a gain to have a
   common dynamic scaling approach that includes network-driven scaling
   and power-shortage scaling.

3.2.  Transport

   Transport protocols make it possible for communication flows to
   adjust themselves to the dynamic conditions that exist in the network
   at any given time: available bandwidth, delays, congestion, the
   ability of a peer to send or receive traffic, and so on.  Depending
   on the conditions, an individual flow may carry traffic at widely
   different rates, may pause for some time, etc.

   This behavior has an effect on sustainability, e.g., in what periods
   the endpoint and network systems are active or when they could be in
   reduced activity or sleep states.  Cellular networks and mobile links
   can scale their energy usage based on load and enter a low-power
   state when a traffic flow ends.  Thus, in theory, the faster the data
   is transferred, the faster the device transmission/reception
   functions can enter a low-power state.

3.2.1.  Motivation

   Transport behavior would have a possibility of impacting how much
   downtime or sleep can be had in the communication system, either on
   the end systems or routers or other equipment in between.  The
   savings can be significant, at least in wireless systems.

   Improvements through transport behavior are only useful if the
   involved systems have power proportionality.






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3.2.2.  Analysis

   Various higher-level transport solutions may also cache or pre-fetch
   information.  For instance, [I-D.irtf-nmrg-green-ps] lifts CDNs as
   one example of technology that has reduced energy consumption, by
   moving the needed endpoints closer to each other.

   On a given set of endpoints, application behavior can impact
   environmental costs.  For instance,
   [I-D.pignataro-enviro-sustainability-consid] observes the effect of
   protocol chattiness.  Does the protocol rely on periodic updates or
   heartbeat messages?  Could such message patterns result in preventing
   links or nodes from going to sleep (absent other communications), and
   in such a case, would an alternative pattern be feasible?

   Transport layer protocol behavior also has an impact.  A critical
   issue is the tradeoff involved in sending traffic.  As argued in
   [NotTradeOff], reducing the amount of time the endpoints and the
   network are active can sometimes help save energy.  As a result, in
   general, delivering information as rapidly as possible would appear
   to be desirable.

   On the other hand, would such as rapid transmission impact peak
   traffic, and as such, contribute to a need to dimension networks for
   higher traffic volumes?  And in this case the need could be only a
   perceived one as a less rapid transmission would not have impacted,
   for instance, a user's ability to view a video if the transmission
   was merely for the buffering of the rest of the video.

   Furthermore, bandwidth-intensive applications can influence other
   applications or users by presenting a significant load on the
   network, and consequently reducing capacity available for others, or
   increasing buffering (and with it, latency) across the network path.
   For an application with intermittent data transfers, such as
   streaming video, this would seem to speak in favor of sustained but
   lower-rate delivery instead of transmitting short high-rate bursts
   [Sammy].  However, this is in contradiction with the energy-saving
   approach above.  Thus, the tradeoff is: should data be sent in a way
   that is "friendly" to others (avoiding bad interference), or should
   it save energy by sending fast, increasing the chance for equipment
   to enter a "sleep" state?

   At the time of writing, the common choice for video is to opt for
   higher rate delivery, potentially saving energy, and possibly at the
   expense of other traffic.  For non-urgent data transfers, the IETF-
   recommended default approach is the opposite: the LEDBAT congestion
   control mechanism [RFC6817], which is designed for such use, will
   always "step out of the way" of other traffic, giving it a low rate



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   when it competes with any other traffic.  Alternatively, if the goal
   is to reduce energy, such traffic could be sent at a high rate, at a
   strategically good moment within a longer time interval; this would
   give network equipment an opportunity to enter a sleep state in the
   remaining time period within the interval.

   A hypothesis could be made that transport protocols should take
   energy into account in addition to the many other inputs they decide
   upon.  For example, it is possible that a non-urgent data transfer
   would send as much as possible as soon as possible when at least one
   of the links along the path is known to be power proportional (e.g.,
   a cellular link), while tracking buffer growth from transmission
   delays to scale back if delay should occur.

   Such ideas remain to be confirmed with experiments, however.

   Similarly, caching and pre-fetching designs need to consider not only
   the likelihood of having acquired the right content in memory, but
   also the sustainability cost of possibly fetching too much or the
   timing of those fetching operations.

   In general, information about the impacts of loading or not loading
   the network with additional traffic, and whether a certain sending
   pattern enables power savings through sleep modes, would be
   beneficial for the communicating endpoints.  Mechanisms for making
   such information available to the endpoints would be useful.

3.2.3.  Recommendation

   As can be seen from the above, there are a number of complex
   tradeoffs merely for transport protocol behavior on a given
   connection.

   This prompts us to give two types of advice.  The first type of
   advice is for protocol designers: simple models are unlikely to
   guarantee optimal results, but as long as normal precautions such as
   congestion control, monitoring queue build-up, and avoiding
   unnecessary messages are employed, systems will operate reasonably
   well.












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   The second type of advice is for further work in the research
   community to better understand what strategies would actually provide
   the best end-user and energy performance, and whether the choice of
   strategy depends on other factors, such as whether sleep modes are
   implemented in network nodes.  There is a clear need for simulations
   and experiments to understand this better.  This may be work that
   fits within the IRTF SUSTAIN research group.  Also, new standards may
   be needed if information sharing about the sustainability and sleep
   mode characteristics of network systems is needed for applications to
   make the best transport decisions.

3.3.  Equipment Longevity

   This section discusses the ability to extend the useful life of
   protocols and/or network equipment in order to amortize the embedded
   energy costs over a longer period, even though it may mean that the
   protocols/equipment may not be fully optimized for the present use.
   This includes devising tools to inform network administrators and
   their users of the potential benefits of network equipment upgrades,
   so that they can make better choices on what upgrades are necessary
   and when.

   It should be noted that from an environmental sustainability
   perspective, it may not always be the best choice to upgrade network
   equipment whenever slightly less power-hungry and "greener"
   alternatives become available.  The environmental cost of amortizing
   the carbon embedded inside equipment over its lifetime, including the
   carbon associated with the manufacturing of the equipment that is to
   be replaced, should be taken into consideration as well.

3.3.1.  Motivation

   Embedded carbon and raw materials can be a significant part of the
   overall environmental impact of systems.  If this can be improved for
   devices that are manufactured in large quantities, the improvements
   can be significant.

   The more the world moves toward low-carbon energy sources, the more
   the manufacturing matters in the holistic view.  Today there can be
   an order of magnitude difference in average emissions for a kWh of
   electricity between two countries.  Thus, any estimates that seek to
   compare the manufacturing and use phase emissions of a network
   equipment would have to be calculated per country or region, and
   there is no universal standard for the whole planet.







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   Long equipment lifetimes are only useful if the longer lifetimes can
   be achieved without compromising other aspects of sustainability,
   such as when using a high-end and power-hungry router in place of
   small routers.  The exact moment when a hardware change is warranted
   for sustainability differs between countries and regions.

3.3.2.  Analysis

   When we engineer protocols and network equipment, we are inclined to
   design them in a highly optimized manner for a very specific set of
   requirements, use cases and context.  While this is necessary in
   certain cases (e.g. constrained nodes with limits on processing
   capacity or long-lived battery powered devices), there are certainly
   cases where such optimized equipment is not absolutely required.
   Most infrastructure network nodes on the Internet utilize only a
   fraction of their design capacity most of the time.

   Designing the equipment with an eye on longevity comes with a set of
   advantages:

   *  It allows the same equipment and protocols be reused in a
      different context in the future. e.g. A core router of today can
      become an edge router in a near future and an access router in the
      further future if the protocol implementations are adaptable.

   *  It can reduce complexity in implementations as well as in network
      management that are usually inherent in highly optimized systems

   *  It can let network equipment operate for a longer period and can
      reduce the frequency of hardware upgrades, in turn reducing the
      environmental impact associated with manufacturing, transporting,
      and disposing of the old/new hardware.

   *  One key disadvantage may be that not optimizing may result in the
      need for premature upgrades for capacity and this needs to be
      considered.

   Hence, it is very likely that extending the life of protocols and
   equipment with higher flexibility could provide a better
   environmental benefit than tightly optimizing only for today’s uses.











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   Another aspect that can play an important role in extending the
   longevity of equipment concerns software-defined networking, in the
   sense of designing networking equipment in such a way that new
   equipment capabilities and features can be introduced via software
   upgrades as opposed to requiring hardware replacement.  This requires
   system architectures that incorporate the necessary infrastructure to
   support such upgrades in a secure manner that does not compromise
   equipment integrity.

   On the other hand, it is very much possible that there could be new
   equipment available that is significantly more sustainable in its
   operation.  The longevity of the existing equipment and the
   amortization of its embedded sustainability costs, needs to be
   balanced against the potential operational savings to be realized by
   upgrading to newer equipment over the intended lifecycle of the newer
   equipment.

3.3.3.  Recommendation

   The guidelines above should be considered for any new system design.
   If some aspect of protocol or network equipment design choice could
   be made more generic and flexible without a significant performance
   and sustainability impact, it needs to be studied in further detail.
   Specifically, the potential additional sustainability costs due to
   forgoing optimization need to be weighed against the potential
   savings in embedded carbon and raw material costs brought about by
   premature upgrades.

   There are also cases where equipment upgrades are done to provide
   better peak performance characteristics (e.g. higher advertised
   speeds towards consumers) and these need to be viewed as well with
   the same tradeoffs in mind.  Also, when newer more sustainable
   equipment is available there needs to be a cost benefit analysis made
   to decide whether to keep current equipment running for longer or
   upgrade to realize the benefits of newer equipment even though it
   incurs new embedded costs.

   Finally, when designing networks, it is recommended to consider
   whether it is possible to reuse retiring equipment in a different
   location or for a different function (e.g. move it to lower traffic
   geographies, core routers become edge/access routers etc.)

3.4.  Encoding

   This is about considering the effects encoding methods on
   sustainability, such as the use of binary encodings instead of text.





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3.4.1.  Motivation

   Better encoding can obviously reduce the length of messages sent or
   reduce the amount of computing required for the encoding and decoding
   operations.  It remains a question mark how big overall impact this
   is, however.  It should only be performed if it gives a measurable
   overall impact.

3.4.2.  Analysis

   Better encoding methods are clearly beneficial for improving the
   detailed-level effectiveness of communications.

   The main questions are, however:

   *  How large are the potential remaining savings in this area, and
      how do they compare to other things?  Particularly considering
      that much of the traffic on the Internet is video, which is
      already highly optimized and constantly updated with better
      encoding methods.  Moran et al. argued in their 2022 paper
      [CBORGreener] [RFC9547] that that for a weather data example from
      [RFC8428] [RFC9193] there are significant savings.  However, this
      needs more research in terms of the overall impact across
      different examples and the general make up of Internet traffic.

   *  At what layer is the compactness achieved?  Are link, IP, or
      transport layer mechanisms that can compact some of the verbose
      messaging useful, or should each protocol have optimal compacting?

   *  Tradeoffs related to compute required to do encoding and decoding
      operations.  These can be relatively heavy operations,
      particularly if compression is performed, particularly if AI-based
      computationally expensive methods are used.

3.4.3.  Recommendation

   More research is needed to quantify the likely sources of measurable
   impacts.

   Of course, new protocols can generally be designed to work with
   compact encoding, unless there is a significant reason not to.  But
   efforts to modify existing protocols for the sake of encoding
   efficiency should be further investigated by the above-mentioned
   quantification results.

   One particular area of interest is the impact of AI-based compression
   methods and their computational and energy costs vs. achieved savings
   in communication efficiencies.



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3.5.  Sustainable by Design: Data Governance Perspective

   Incorporating sustainability into the design phase of network
   architecture is critical for ensuring long-term environmental and
   operational benefits.  From a Data Governance point of view,
   "Sustainable by Design" involves embedding sustainability principles
   and practices into the data management frameworks and processes from
   the outset.

3.5.1.  Motivation

   Data governance plays a pivotal role in shaping how data is
   collected, stored, processed, and used.  By integrating
   sustainability into these processes, organizations can ensure that
   their data practices contribute to environmental goals, such as
   reducing carbon footprints, optimizing resource usage, and minimizing
   waste.

3.5.2.  Analysis

   Key elements of Sustainable by Design in data governance include:

   *  Data Minimization: Collecting only the data that is necessary and
      useful, reducing storage and processing requirements, which in
      turn lowers energy consumption.

   *  Efficient Data Storage Solutions: Implementing energy-efficient
      data storage technologies and practices that prioritize reduced
      power usage and cooling needs.

   *  Lifecycle Management: Ensuring that data is managed throughout its
      lifecycle in a way that minimizes environmental impact, including
      secure and sustainable data disposal practices.

   *  Transparency and Accountability: Establishing clear data
      governance policies that promote transparency in data usage and
      accountability for sustainability objectives.

3.5.3.  Recommendation

   Organizations should adopt data governance frameworks that
   incorporate sustainability as a core principle.  This includes
   setting clear sustainability goals, measuring progress towards these
   goals, and continuously improving data management practices to
   enhance sustainability.  By doing so, organizations can ensure that
   their data operations are not only effective but also environmentally
   responsible.




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   There is a protocol designer angle in this as well.  Protocol
   designers should consider at least the data minimization aspects from
   Section 3.5.2, and may additionally consider providing mechanisms for
   the lifecycle management and transparency aspects.

4.  Recommendations for Protocol Design

   The recommendations that can be applied by protocol designers and
   architects have been listed in Section 2 and Section 3.
   Specifically:

   *  Measurement and modeling are a necessary foundation to understand
      where environmental impacts are generated, and to quantify any
      improvements.  The recommendations related to this topic were
      listed in Section 2.1.3.  These are primarily about ensuring that
      the measurement frameworks are generic enough to support data
      collection for an evolving set of metrics, and to prepare for the
      possibility that mathematical modeling may have to replace
      measurements in some cases.

   *  Dynamic scaling is the ability to respond to demand variations and
      resiliency requirements while optimizing energy consumption
      clearly has significant potential for savings.  Recommendations
      related to this were listed in Section 3.1.3.  These are about
      some basic techniques for being able to scale systems up and down
      while avoiding negative effects from these operations.

   *  Transport-related recommendations were listed in Section 3.2.
      These are about tradeoffs associated with different transport
      strategies.

   *  Longevity-related recommendations were listed in Section 3.3.3.
      These are primarily about how equipment can fulfill evolving roles
      over its lifetime, and associated tradeoffs.

   *  Encoding-related recommendations were listed in Section 3.4.3.
      These are about the effects of encoding size in protocols, and the
      associated compression computing impacts.

   *  Data governance-related recommendations were listed in
      Section 3.5.3.  These are primarily about ensuring the right
      amount of data is collected, stored, and processed, in view of the
      effort required to do so.








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5.  Recommendations for Further Work and Research

   There are several areas where concrete advice for protocol designers
   could not be given, or additional advice would be useful, but we do
   not understand the situation well enough to give practical advice.

   These include:

   *  Past and ongoing work in various systems and protocols has looked
      at dynamic scaling extensively, but we believe work also remains.
      Any large-scale system likely benefits from further analysis,
      unless already ongoing.  Guidance in Section 3.1 simple, and
      further work in detailing this guidance would also be useful.

   *  Transport-related optimizations (see Section 3.2) that enable
      devices to consume less power by sleeping more appear to have
      potential for significant savings but confirming this requires
      further research.  Such research could be performed in the context
      of the recently chartered SUSTAIN research group.

   *  More research is needed to quantify the likely sources of
      measurable impacts when it comes to efficient protocol message
      encoding discussed in Section 3.4.  Also, the tradeoffs involving
      the use AI-based compression methods deserve further study.
      Again, these are topics that the research group could take on.

6.  Security Considerations

   It is possible that the introduction of features and architectural
   properties to facilitate environmentally sustainable Internet
   technology introduces new attack vectors or other security
   ramifications.

   For example, the introduction of measurements and metrics for the
   purpose of saving energy could be misused for the opposite effect
   when compromised.  For example, measurements might be tampered with
   in order to cause an operator to waste energy.  Energy measurements,
   when abused, might also result in compromised security, for example
   by allowing to infer usage profiles.  They could also be abused to
   implement a covert communications channel in which information is
   leaked via tampered measurement values that are being reported.

   Networking features and technology choices may have security
   implications regardless of why they are introduced, including for
   reasons of environmental sustainability.  The possibility of this
   needs to be taken into consideration, understood, and communicated to
   allow for their mitigation.




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7.  IANA Considerations

   This document has no IANA actions.

8.  Informative References

   [Baseline] Livieratos, S., Panetsos, S., Fotopoulos, A., and M.
              Karagiorgas, "A New Proposed Energy Baseline Model for a
              Data Center as a Tool for Energy Efficiency Evaluation",
              International Journal of Power and Energy Research, Vol.
              3, No. 1 , April 2019.

   [BenchmarkingFramework]
              Mahadevan, P., Sharma, P., Banerjee, S., and P.
              Ranganathan, "A Power Benchmarking Framework for Network
              Devices", In L. Fratta et al. (Eds.): NETWORKING 2009,
              LNCS 5550, pp. 795–808 , 2009.

   [CBORGreener]
              Moran, B., Birkholz, H., and C. Bormann, "CBOR is Greener
              than JSON", Position paper in the 2022 IAB Workshop
              Environmental Impact of Internet Applications and
              Systems , October 2022.

   [Experiment]
              Groningen, J. and C. Lung, "Green Network Traffic
              Engineering Using Segment Routing: An Experiment Report",
              2024 20th International Conference on Network and Service
              Management (CNSM) , 2024.

   [I-D.cparsk-eimpact-sustainability-considerations]
              Pignataro, C., Rezaki, A., Krishnan, S., ElBakoury, H.,
              and A. Clemm, "Sustainability Considerations for
              Internetworking", Work in Progress, Internet-Draft, draft-
              cparsk-eimpact-sustainability-considerations-07, 24
              January 2024, <https://datatracker.ietf.org/doc/html/
              draft-cparsk-eimpact-sustainability-considerations-07>.

   [I-D.ietf-tvr-alto-exposure]
              Contreras, L. M., "Using off-path mechanisms for exposing
              Time-Variant Routing information", Work in Progress,
              Internet-Draft, draft-ietf-tvr-alto-exposure-02, 5 July
              2025, <https://datatracker.ietf.org/doc/html/draft-ietf-
              tvr-alto-exposure-02>.

   [I-D.ietf-tvr-requirements]
              King, D., Contreras, L. M., Sipos, B., and L. Zhang, "TVR
              (Time-Variant Routing) Requirements", Work in Progress,



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              Internet-Draft, draft-ietf-tvr-requirements-06, 7 July
              2025, <https://datatracker.ietf.org/doc/html/draft-ietf-
              tvr-requirements-06>.

   [I-D.ietf-tvr-schedule-yang]
              Qu, Y., Lindem, A., Kinzie, E., Fedyk, D., and M.
              Blanchet, "YANG Data Model for Scheduled Attributes", Work
              in Progress, Internet-Draft, draft-ietf-tvr-schedule-yang-
              05, 4 July 2025, <https://datatracker.ietf.org/doc/html/
              draft-ietf-tvr-schedule-yang-05>.

   [I-D.irtf-nmrg-green-ps]
              Clemm, A., Pignataro, C., Westphal, C., Ciavaglia, L.,
              Tantsura, J., and M. Odini, "Challenges and Opportunities
              in Management for Green Networking", Work in Progress,
              Internet-Draft, draft-irtf-nmrg-green-ps-06, 15 March
              2025, <https://datatracker.ietf.org/doc/html/draft-irtf-
              nmrg-green-ps-06>.

   [I-D.pignataro-enviro-sustainability-architecture]
              Pignataro, C., Rezaki, A., Krishnan, S., Arkko, J., Clemm,
              A., ElBakoury, H., and S. Prabhu, "Architectural
              Considerations for Environmental Sustainability", Work in
              Progress, Internet-Draft, draft-pignataro-enviro-
              sustainability-architecture-02, 12 May 2025,
              <https://datatracker.ietf.org/doc/html/draft-pignataro-
              enviro-sustainability-architecture-02>.

   [I-D.pignataro-enviro-sustainability-consid]
              Pignataro, C., Rezaki, A., Arkko, J., Clemm, A.,
              ElBakoury, H., and S. Prabhu, "Sustainability
              Considerations for Networking Protocols and Applications",
              Work in Progress, Internet-Draft, draft-pignataro-enviro-
              sustainability-consid-02, 12 May 2025,
              <https://datatracker.ietf.org/doc/html/draft-pignataro-
              enviro-sustainability-consid-02>.

   [LCAandUsage]
              Weppe, O., Bekri, D., Guibert, L., Aubet, L., Prévotet,
              J., Pelcat, M., and S. Rumley, "Carbon Topography
              Representation: Improving Impacts of Data Center
              Lifecycle", Proceedings of the 4th Workshop on Sustainable
              Computer Systems (HotCarbon'25) , 2025.








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   [LinkAggregation]
              "IEEE Standard for Local and Metropolitan Area Networks--
              Link Aggregation", IEEE STD 802.1AX-2020 (Revision of IEEE
              STD 802.1AX-2014): 1–333. doi:10.1109/
              IEEESTD.2020.9105034. ISBN 978-1-5044-6428-4 , May 2020.

   [LoadShifting]
              Mathew, V., Sitaraman, R. K., and P. Shenoy, "Reducing
              energy costs in Internet-scale distributed systems using
              load shifting", Sixth International Conference on
              Communication Systems and Networks (COMSNETS), Bangalore,
              India, pp. 1-8, doi: 10.1109/COMSNETS.2014.6734894 , 2014.

   [Modeling] Chen, C., Barrera, D., and A. Perrig, "Modeling Data-Plane
              Power Consumption of Future Internet Architectures", IEEE
              2nd International Conference on Collaboration and Internet
              Computing (CIC), Pittsburgh, PA, USA, pp. 149-158, doi:
              10.1109/CIC.2016.031 , 2016.

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

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,
              <https://www.rfc-editor.org/rfc/rfc6817>.

   [RFC8428]  Jennings, C., Shelby, Z., Arkko, J., Keranen, A., and C.
              Bormann, "Sensor Measurement Lists (SenML)", RFC 8428,
              DOI 10.17487/RFC8428, August 2018,
              <https://www.rfc-editor.org/rfc/rfc8428>.

   [RFC9193]  Keränen, A. and C. Bormann, "Sensor Measurement Lists
              (SenML) Fields for Indicating Data Value Content-Format",
              RFC 9193, DOI 10.17487/RFC9193, June 2022,
              <https://www.rfc-editor.org/rfc/rfc9193>.

   [RFC9547]  Arkko, J., Perkins, C. S., and S. Krishnan, "Report from
              the IAB Workshop on Environmental Impact of Internet
              Applications and Systems, 2022", RFC 9547,
              DOI 10.17487/RFC9547, February 2024,
              <https://www.rfc-editor.org/rfc/rfc9547>.





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   [RFC9657]  Birrane, III, E., Kuhn, N., Qu, Y., Taylor, R., and L.
              Zhang, "Time-Variant Routing (TVR) Use Cases", RFC 9657,
              DOI 10.17487/RFC9657, October 2024,
              <https://www.rfc-editor.org/rfc/rfc9657>.

   [Sammy]    Bruce Spang, Shravya Kunamalla, Renata Teixeira, Te-Yuan
              Huang, Grenville Armitage, Ramesh Johari, and Nick
              McKeown, "Sammy: smoothing video traffic to be a friendly
              internet neighbor", In Proceedings of the ACM SIGCOMM 2023
              Conference (ACM SIGCOMM '23). Association for Computing
              Machinery, New York, NY, USA, 754–768.
              https://doi.org/10.1145/3603269.3604839 , 2023.

   [Segment]  Lung, C. and H. ElBakoury, "Exploiting Segment Routing and
              SDN Features for Green Traffic Engineering", IEEE 8th
              International Conference on Network Softwarization
              (NetSoft), Milan, Italy, pp. 49-54, doi: 10.1109/
              NetSoft54395.2022.9844091 , 2022.

   [Unifying] Ishii, K., Kurumida, J., K.-i Sato, Kudoh, T., and S.
              Namiki, "Unifying Top-Down and Bottom-Up Approaches to
              Evaluate Network Energy Consumption", In Journal of
              Lightwave Technology, vol. 33, no. 21, pp. 4395-4405, doi:
              10.1109/JLT.2015.2469145 , November 2015.

   [UNSDG]    "United Nations Sustainable Development Goals",
              https://unstats.un.org/sdgs , 2017.

Appendix A.  Modeling Approaches and Literature

   The paper [Modeling] provides a model for IP Routers and the routers
   of other future Internet architectures (FIA) such as SCION and
   NEBULA.  They use a generic model which captures the commonalities of
   IP router as well as the peculiarities of FIA routers.  They conduct
   a large-scale simulation based on this router model to estimate the
   power consumption for different network architectures.

   Since routers and other network devices and functions can be
   virtualized, this article (1) provides comprehensive "graphical,
   analytical survey of the literature, over the period 2010–2020, on
   the measurement of power consumption and relevant power models of
   virtual entities as they apply to the telco cloud."  This paper A
   Methodology and Testbed to Develop an Energy Model for 5G Virtualized
   RANs IEEE Conference Publication IEEE Xplore got best paper award for
   GreenNet 2024, but I am not sure if we are interested to model 5G
   vRAN.





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   There is a plethora of publications on modeling communication
   networks and DC computing.

A.1.  Customer Attribution

   When organizations assess their Scope 3 emissions, they need to sum
   up their share of emissions from all their suppliers, one of which
   for example, might be a cloud hosting service.  In order for the
   supplier to provide an emission share value back to the customer, the
   provider needs to develop a mechanism for attribution.

   A significant challenge in accurately assessing Scope 3 emissions is
   avoiding Double Counting, where the same emission is reported by
   multiple entities.  According to the GHG Protocol best practices, it
   is crucial to establish clear guidelines and agreements between
   suppliers and customers to ensure that emissions are attributed
   correctly and not counted multiple times.  This requires transparent
   communication and precise emission reporting standards to ensure that
   all parties involved have a consistent understanding of which
   emissions belong to which organization.

   By addressing the Double Counting issue, companies can achieve more
   accurate and reliable Scope 3 emissions assessments, thereby
   contributing to better overall sustainability reporting and
   improvement efforts.

A.2.  Baselining and Benchmarking

   Establishing a baseline is a fundamental step in the process of
   improving energy efficiency and sustainability of network technology.
   Baselining involves establishing a reference point of typical energy
   usage, which is crucial for identifying inefficiencies and measuring
   improvements over time.  In this step, the controller uses only the
   collected data from datasheets and other reliable sources.

   By establishing a baseline and using benchmarking, organizations can
   determine if their networking equipment is performing normally or if
   it is deviating from expected performance.  This is the first step in
   identifying and guiding necessary improvements.  Benchmarking
   involves collecting performance measurements of networking equipment
   under controlled conditions.  This process helps establish
   standardized performance metrics, allowing for comparison against
   baselines collected during regular operational conditions.

   The initial measurement of networking equipment's energy efficiency
   and performance, known as Baselining, should be coordinated with
   vendor specifications and industry standards to understand what is
   considered normal or optimal performance.  For example, if the



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   baseline indicates that your switches operate at 5 Gbps per watt,
   while vendor specifications suggest 8 Gbps per watt and the industry
   standard is 10 Gbps per watt, actions should be taken to implement
   energy-saving measures and upgrades.  Continuously tracking
   subsequent measurements can reveal if efficiency improves towards the
   benchmark of 8-10 Gbps per watt.

   This practice ensures that any improvements can be quantifiably
   tracked over time, providing a clear measure of the effectiveness of
   the implemented changes and guiding further enhancements in network
   sustainability.

   See also [Baseline] and [BenchmarkingFramework].

Acknowledgments

   Everyone on the author section has contributed to the document in
   significant ways.  The author list has been ordered in (reverse)
   alphabetical order.

   Parts of this document extensively leverage ideas and text from
   [I-D.cparsk-eimpact-sustainability-considerations] and
   [I-D.pignataro-enviro-sustainability-architecture] and associated
   discussions in the IETF, IRTF, and IAB groups.  We acknowledge and
   appreciate the many contributors whose work has enhanced its
   development.

Authors' Addresses

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


   Emile Stephan
   Orange
   Email: emile.stephan@orange.com


   Eve Schooler
   University of Oxford
   Email: eve.schooler@gmail.com


   Sebastien Rumley
   HES-SO
   Email: sebastien.rumley@hes-so.ch




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   Ali Rezaki
   Nokia
   Email: ali.rezaki@nokia.com


   Jukka Manner
   Aalto University
   Email: jukka.manner@aalto.fi


   Carlos Pignataro
   Blue Fern Consulting
   Email: cpignata@gmail.com


   Marisol Palmero
   Cisco
   Email: mpalmero@cisco.com


   Jan Lindblad
   All For Eco
   Email: jan.lindblad+ietf@for.eco


   Suresh Krishnan
   Cisco
   Email: sureshk@cisco.com


   Ari Keränen
   Ericsson
   Email: ari.keranen@ericsson.com


   Hesham ElBakoury
   Email: helbakoury@gmail.com


   Luis M. Contreras
   Telefonica
   Email: contreras.ietf@gmail.com


   Alexander Clemm
   Independent
   Email: ludwig@clemm.org




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   Jari Arkko
   Ericsson
   Email: jari.arkko@gmail.com
















































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