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Challenges and Opportunities in Management for Green Networking

Document Type Active Internet-Draft (individual)
Authors Alexander Clemm , Cedric Westphal , Jeff Tantsura , Laurent Ciavaglia , Marie-Paule Odini
Last updated 2023-03-13
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Network Working Group                                           A. Clemm
Internet-Draft                                               C. Westphal
Intended status: Informational                                 Futurewei
Expires: 14 September 2023                                   J. Tantsura
                                                            L. Ciavaglia
                                                              M-P. Odini
                                                           13 March 2023

    Challenges and Opportunities in Management for Green Networking


   Reducing technology's carbon footprint is one of the big challenges
   of our age.  Networks are an enabler of applications that reduce this
   footprint, but also contribute to this footprint substantially
   themselves.  Many of the biggest opportunities to reduce this
   footprint may not be management or even networking specific, for
   instance general power efficiency gains in hardware or deployment of
   equipment in more energy-efficient buildings.  However, methods to
   make networking technology itself "greener" and in particular to
   manage networks in ways that reduces their carbon footprint without
   impacting their utility also 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" and reduce
   network carbon footprint.

Status of This Memo

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

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 14 September 2023.

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Copyright Notice

   Copyright (c) 2023 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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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   6
   3.  Contributors to Network Energy Consumption  . . . . . . . . .   7
     3.1.  Power Consumption Characteristics . . . . . . . . . . . .   7
     3.2.  Dimensioning for Peak Usage . . . . . . . . . . . . . . .   8
   4.  Challenges and Opportunities - Equipment Level  . . . . . . .   9
     4.1.  Hardware and Manufacturing  . . . . . . . . . . . . . . .   9
     4.2.  Visibility and Instrumentation  . . . . . . . . . . . . .  10
   5.  Challenges and Opportunities - Protocol Level . . . . . . . .  11
     5.1.  Protocol Enablers for Carbon Footprint Optimization
           Mechanisms  . . . . . . . . . . . . . . . . . . . . . . .  12
     5.2.  Protocol Optimization . . . . . . . . . . . . . . . . . .  13
     5.3.  Data Volume Reduction . . . . . . . . . . . . . . . . . .  14
     5.4.  Network Addressing  . . . . . . . . . . . . . . . . . . .  15
   6.  Challenges and Opportunities - Network Level  . . . . . . . .  16
     6.1.  Network Optimization and Energy/Carbon/Pollution-Aware
           Networking  . . . . . . . . . . . . . . . . . . . . . . .  16
     6.2.  Assessing Carbon Footprint and Network-Level
           Instrumentation . . . . . . . . . . . . . . . . . . . . .  17
     6.3.  Convergence Schemes . . . . . . . . . . . . . . . . . . .  18
   7.  Challenges and Opportunities - Architecture Level . . . . . .  19
   8.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  21
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  21
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  21
   11. Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  22
   12. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  22
   13. Informative References  . . . . . . . . . . . . . . . . . . .  22
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  26

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1.  Introduction

   Climate change and the need to curb greenhouse emissions have been
   recognized by the United Nations and by most governments as one of
   the big challenges of our time.  As a result, improving energy
   efficiency and reducing power consumption are becoming of increasing
   importance for society and for many industries.  The networking
   industry is no exception.

   Arguably, networks can already be considered "green" technology in
   that networks enable many applications that allow users and whole
   industries to save energy and become more sustainable in a
   significant way.  For example, it allows (at least to an extent) to
   replace travel with teleconferencing; it enables many employees to
   work from home and "telecommute," thus reducing the need for actual
   commute; IoT applications that facilitate automated monitoring and
   control from remote sites help make agriculture more sustainable by
   minimizing the application of resources such as water and fertilizer;
   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.

   The IETF has recently initiated a reflection on the energy cost of
   hosting meetings three times a year (see for instance  It conducted a
   study of the carbon emissions of a typical meeting, and found out
   that 99% of the emissions were due to the 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 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 by also reducing its own.  Future
   networking advances will increasingly need to focus on becoming more
   energy-efficient and reducing carbon footprint, both for economic
   reasons and for reasons of corporate responsibility.  This shift has
   already begun and sustainability is already becoming an important
   concern for network providers.  In some cases such as in the context
   of networked data centers, the ability to procure enough energy
   becomes a bottleneck prohibiting further growth and greater
   sustainability thus becomes a business necessity.

   For example, in its annual report, Telefónica reports that in 2021,
   its network's energy consumption per PB of data amounted to 54MWh
   [telefonica2021].  This rate has has been dramatically decreasing (a

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   seven-fold factor over six years) although gains in efficiency are
   being offset by simultaneous growth in data volume.  In the same
   report, it is stated as an important corporate goal to continue on
   that trajectory and aggressively reduce overall carbon emissions

   Perhaps the most obvious gains in sustainability can be made with
   regards to improving the efficiency with which networks utilize
   power, reducing the amount of energy that is required to provide
   communication services.  However, for a holistic approach other
   aspects need to be considered as well.  Perhaps most importantly,
   carbon footprint is determined not by it power consumption alone.
   The sustainability of power sources needs to be taken into account as
   well.  A deployment that includes devices that are less energy-
   efficient but that are powered by a sustainable energy source can
   arguably be considered "greener" than a deployment that includes
   highly-efficient device that are powered by Diesel generators.  In
   fact, in the same Telefónica report, extensive reliance on renewable
   energy sources is emphasized.

   Similarly, deployments can take other environmental factors into
   account that affect carbon footprint.  For example, deployments in
   which factors such as the need for cooling are reduced 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.  Finally, manufacturing and
   recycling of 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.

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

   *  At the 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.  However, beyond making those
      appliances merely more energy-efficient, there are other important
      ways in which equipment can help networks become greener.  This
      includes aspects such as support for port power saving modes
      allowing to reduce power consumption for resources that are not
      fully utilized, but also management instrumentation that allows to
      precisely monitor power usage at different levels of granularity.

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      This enables (for example) controller applications that aim to
      optimize energy usage across the network.  (As a side note, the
      term "device", as used in the context of this draft, is used to
      refer to networking equipment.  We are not taking into
      consideration end-user devices and endpoints such as mobile phones
      or computing equipment.)

   *  At the 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 to
      allow not only for better network utilization, but 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.  Likewise, aspects such as restructuring
      addresses in ways that allow to minimize the size of lookup tables
      and associated memory sizes and their energy use can play a role
      as well.  Another role of protocols concerns the enabling of
      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.

   *  At the network level.  Perhaps the greatest opportunities to
      realize power savings exist at the level of the network as whole.
      For example, optimizing energy efficiency may involve directing
      traffic in such a way that it allows for isolation of equipment
      that may at the moment not be needed so that it could 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 be rated less "green" and more power-
      intensive than others or powered by less-sustainable energy
      sources.  Their use might be avoided unless required to meet 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 of network path optimization.

   *  At the architecture level.  The current network architecture
      supports a wide range of applications, but does not take into
      account energy efficiency as one of its design parameters.  One
      can argue that the most energy efficient shift of the last two

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      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?

   We believe that network standardization organizations in general, and
   IETF in particular, can make important contributions to each of these
   vectors.  In this document, we will therefore explore each of those
   vectors in further detail and for each point out specific challenges
   for IETF.  As our starting point, we borrow some material from a
   prior paper, [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).  In addition, this document focuses on and attempts to
   articulate specific challenges relating to work that could be
   championed by the IETF to make a difference.

2.  Definitions and Acronyms

   Below you find acronyms used in this draft:

      Carbon footprint: as used in this document, the amount of carbon
      emissions associated with the use or deployment of technology,
      usually directly correlated with the associated energy

      CDN: Content Delivery Network.

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

      DC: Data Center.

      FCT: Flow Completion Time.

      HVAC: Heating, Ventilation, Air Conditioning.

      ICN: Information Centric Network.

      IGP: Interior Gateway Protocol.

      IPU: Infrastructure Processing Units.

      LEO: Low Earth Orbit.

      LPM: Longest Prefix Match, a method to look up prefixes in a
      forwarding element.

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      MPLS: Multi-Path Label Switching

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

      NIC: Network Interface Card.

      QoS, QoE: Quality of Service, Quality of Experience.

      QUIC: Quick UDP Internet Connections.

      SDN: Software-Defined Networking.

      TCP: Transport Control Crotocol.

      TE: Traffic Engineering.

      WAN: Wide Area Network.

3.  Contributors to Network Energy Consumption

   Carbon footprint and, with it, greenhouse gas emissions are
   determined by a number of 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 energy
   mix into account.  Likewise, there are other factors such as hidden
   carbon cost reflecting the carbon footprint expended in manufacturing
   of networking hardware.  Nonetheless, network energy consumption is
   an excellent predictor for carbon footprint and its reduction key to
   sustainable solutions.  Exploring possibilities to improve energy
   efficiency is hence a key factor for greener, more sustainable, less
   carbon-intensive networks.

   For this, it is important to understand which aspects contribute to
   power consumption the most and hence where the greatest potential not
   just for power savings but also sustainability improvements lies.

3.1.  Power Consumption Characteristics

   Power is ultimately drawn from devices.  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.  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

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   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 power over
   the same system running at full load [chabarek08], [cervero19].
   Generally, the cost of sending the first bit is very high, as it
   requires powering up a device, port, etc.  The incremental cost of
   transmission of additional bits (beyond the first) is many orders of
   magnitude lower.  Likewise, the incremental cost of incremental CPU
   and memory needed to process additional packets becomes fairly
   negligible.  This means that a device's power consumption does not
   increase linearly with the volume of forwarded traffic.  Instead, it
   resembles more of a step function in which power consumption stays
   roughly the same up to a certain volume of traffic, followed by a
   sudden jump when when additional resources need to be procured to
   support a higher volume of traffic.  By the same token, generally
   speaking it is more energy-efficient to transmit a large volume of
   data in one burst (and turning off the interface when idling),
   instead of continuously transmitting 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.

3.2.  Dimensioning for Peak Usage

   The implications on green networking from an energy-savings
   standpoint are significant: Potentially the largest gains can be made
   when network resources can effectively be taken off the grid (i.e.
   isolated and removed from service so they can be powered down while
   not needed).  Likewise, for applications where this is possible, it
   may be desirable to replace continuous traffic at low data rates with
   traffic that is sent in burst at high data rates, in order to
   potentially maximize the time during which resources can be idled.

   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 for very short control loops.

   As a result, emphasis needs to be given to technology that allows to
   (for example) (at the device level) exercise very efficient and rapid
   discovery, monitoring, and control of networking resources so that
   they can be dynamically be taken offline or back into service,

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   without (at the network level) requiring extensive convergence of
   state across the network or recalculation of routes and other
   optimization problems, and (at the network equipment level) support
   rapid power cycle and initialization schemes.

4.  Challenges and Opportunities - Equipment Level

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

   *  Hardware and manufacturing.  Related opportunities are arguably
      among the most obvious and perhaps "largest".  However, solutions
      here may lie largely outside IETF's scope.

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

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, have it dissipate less
   heat to consume less energy and reduce the need for cooling, making
   it eco-friendly to deploy, sourcing sustainable materials and
   facilitating recycling of equipment at the end of its life-cycle all
   contribute to making networks greener.  More specific and unique to
   networking are schemes to reduce energy usage of transmission
   technology from wireless (antennas) to optical (lasers).

   One critical aspect of the energy cost of networking is the cost to
   manufacture and deploy the networking equipment.  This is outside of
   the scope of this document: we only consider the energy cost of
   running the network, as this is where the IETF can play a role.
   However, a holistic approach would include into this the embedded
   energy that is included in the networking equipment.  One aspect for
   the IETF may be to consider impact of deploying new protocols on the
   rate of obsolescence of the equipment.  For instance, incremental
   approaches that do not require to replace equipment right away - or
   even extend the lifetime of deployed equipment - would have a lower
   energy footprint.  This is one important benefit also of technologies
   such as Software-Defined Networking and Network Function
   Virtualization, as they may allow support of new networking features
   through software updates without requiring hardware replacements.

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

4.2.  Visibility and Instrumentation

   Beyond "first-order" opportunities as outlined in the previous
   subsection, network equipment just as importantly plays an important
   role to enable and support green networking at other levels.  Of
   prime importance is the equipment's ability to provide visibility to
   management and control plane 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 used to optimize
   forwarding decisions.

   One prerequisite to such schemes is to have proper instrumentation in
   place that allows to monitor current power consumption at the level
   of networking devices as a whole, line cards, and individual ports.
   Such instrumentation should also allow to assess 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 as well as 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, an effort to define sets of green
   networking metrics is currently under way
   [I.D.draft-cx-green-metrics].  An agreed set of such metrics will
   provide the basis for further steps such as the implementation of
   corresponding data models as part of management and control

   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

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   could be hosted on containers or virtual machines which 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 could be more power
   efficient and consume power more proportionally 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

   The following summarizes some challenges and opportunities that can
   provide the basis for IETF-led advances:

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

   *  Virtualized energy and pollution metrics and assessment of their
      effectiveness in solutions that optimize carbon footprint also 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 to account for energy mix powering equipment,
      to facilitate solutions that optimize pollution and carbon
      footprint beyond mere energy efficiency [Hossain2019].

5.  Challenges and Opportunities - Protocol Level

   There are several opportunities to improve network sustainability at
   the protocol level.  We characterize them along three main
   categories: protocols that enable carbon footprint optimization
   schemes at the network level, protocols designed to optimize data
   transmission rates under energy considerations, and protocols
   designed to reduce the volume of data to be transmitted.  A fourth
   category concerns aspects related to network addressing schemes.

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5.1.  Protocol Enablers for Carbon Footprint Optimization Mechanisms

   As will be discussed in Section Section 6, energy- 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 view of the topology that is consistent with
   frequent topology updates due to power cycling of the network

   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.

   Some mechanism is needed to present to the management layer a view of
   the network that identifies opportunities to turn resources off
   (routers/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 that can
   provide the basis for IETF-led advances:

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

   *  Assess which protocols could be extended with energy- and
      sustainability-related parameters in ways that would enable
      "greener" networking solutions, and exploring those solutions.

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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 Maximal Link Utilization (MLU)
   but energy saving mechanisms could decide to do the opposite
   (maximize minimal 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 WiFi access.  This should be done
   carefully: in the limit, a 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 that can
   provide the basis for IETF-led advances:

   *  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 smoothen it out
      over longer periods.

   *  Protocol advances that allow to optimize link utilization
      according to different goals and strategies (including maximizing
      minimal link utilization vs minimizing maximal link utilization,

   *  Assessments of the carbon impact of such strategies.

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5.3.  Data Volume Reduction

   The first 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.
   Currently, most of the traffic over the Internet consists of video
   streaming and encoders for video are already quite efficient and keep
   improving all the time, resulting in energy savings as one of many
   advantages (of course being offset by increasingly higher
   resolution).  However, 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

   At the transport protocol layer, TCP and to some extent QUIC react to
   congestion by dropping packets.  This is a highly energy inefficient
   method to signal congestion, since the network has to wait one RTT to
   be aware that the congestion has occurred, and since the effort to
   transmit the packet from the source up until it is dropped ends up
   being wasted.  This calls for new transport protocols that react to
   congestion without dropping packets.  ECN[RFC2481] is a possible
   solution, however not widely deployed.  DC-TCP [alizadeh2010DCTCP] is
   tuned for Data Centers, L4S is an attempt to port similar
   functionality to the Internet [I-D.ietf-tsvwg-l4s-arch].  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 this 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 by
   reducing their header tax, for example applying header compression.
   An example in IETF is [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 large payloads
   close to the MTU (the header tax and any savings here are very
   limited), or whether it sends packets with very small payload size
   (making the header tax more pronounced and savings more significant).

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   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 look-up, thereby reducing the energy
   consumption.  It is unclear if some state at router to speed up look
   up is more energy efficient that "no state + lookup" that 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 look-up
   computation at a router.  It is unclear if Longest Prefix Match (LPM)
   is efficient from an energy point of view or if constitutes a
   significant energy burden for the operation of a router.

   The following summarizes some challenges and opportunities that can
   provide the basis for IETF-led advances in this space:

   *  Assessments of energy-related tradeoffs regarding protocol design
      space and tradeoffs, such as maintaining state versus more compact
      encodings or extra computation for transcodings versus larger data

   *  Protocol advances for improving the ratio of goodput to throughput
      and to reduce waste: reduction in header tax, in protocol
      verbosity, in need for retransmissions, improvements in coding,

5.4.  Network Addressing

   There are other ways to shave off energy usage from networks.  One
   example concerns network addressing.  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.  From an energy footprint perspective,
   both can be considered wasteful and offer opportunities for
   improvement.  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.

   The following summarizes some challenges and opportunities that can
   provide the basis for IETF-led advances in this space:

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

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   *  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 straighforward 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.  It also includes aspects such as
   considering the incremental carbon footprint in routing decisions.
   Optimizing cost has a long tradition in networking; many of the
   existing mechanisms can be leveraged for greener networking simply by
   introducing carbon footprint as a cost factor.  Low-hanging fruit
   include the inclusion of carbon-related parameters as a cost
   parameter in control planes, whether distributed (e.g.  IGP) or
   conceptually centralized via SDN controllers.

   Other opportunities concern adding carbon-awareness to dynamic path
   selection schemes.  This is sometimes also referred to as "energy-
   aware networking" (respectively "pollution-aware networking"
   [Hossain2019] or "carbon-aware networking", when carbon footprint
   related parameters beyond pure 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 currently needed under
   current network demand and load conditions.  Therefore, weaning such
   resources from traffic becomes an important consideration for energy-
   efficient traffic steering.  This 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.  This usually occurs for important reasons, such as
   making networks more resilient, optimizing service levels, and
   increasing fairness.  One of the big challenges hence concerns how
   resource weaning schemes to realize energy savings can be
   accommodated while preventing the cannibalization of other important
   goals, counteracting other established mechanisms, and avoiding
   destabilization of the network.

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   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 nor even at a short time
   scale.  In the access networks connecting to that core, though, there
   are opportunites for this fast convergence: traffic is much more
   bursty, less predictable and the network should be able to be more
   reactive.  Other domains such as DCs may have also more variable
   workloads and different traffic patterns.

   The following summarizes some challenges and opportunities that can
   provide the basis for IETF-led advances in this space:

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

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

   *  Articulate and, as applicable, moderate tradeoffs between carbon
      awareness and other operational goals such as robustness and

   *  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) that allow to easily assess energy cost and carbon
   footprint will be required.  These abstractions need to account for
   not only for the energy cost associated with packet forwarding across
   a given path, but related cost for processing, for memory, for
   maintaining of state, to result in a holistic picture.

   Optimization of carbon footprint involves in many cases 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 an hyperscale DC.  The latter
   is often better optimized than the latter.)  Likewise, other aspects
   of carbon footprint beyond mere energy-intensity should be

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   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 outline in Section 4.2 but relates it also to
   concepts that are meaningful at the network level, i.e. to flows and
   to paths.  For example, it will be useful to provide visibility into
   the carbon intensity of a path: Can the carbon cost of traffic
   transmitted over the path be aggregated?  Does the path include
   outliers, i.e. segments with equipment with a particularly poor
   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 to the ability to raise
   carbon awareness by end users.

   The following summarizes some challenges and opportunities that can
   provide the basis for IETF-led advances in this space:

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

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

6.3.  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 getting 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 is hence not
   susceptible to such schemes.  In order to mitigate this problem,
   mechanisms should be investigate that do not require convergence
   beyond the vicinity of the affected network device.  Especially in
   cases where central network controllers are involved that are
   responsible for aspects such as configuration of paths and the
   positioning of network functions and that aim for global
   optimization, the impact of churn needs to be minimized.  This means
   that, for example, (re-) discovery and update schemes need to be
   simplified and extensive recalculation e.g. of routes and paths based

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   on the current energy state of the network needs to be avoided.

   Challenges and opportunities for IETF-led advances in this space

   *  Protocols that facilitate rapid convergence (per section
      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.

7.  Challenges and Opportunities - Architecture Level

   Another possibility to improve network energy efficiency is to
   organize networks in a way that they can best serve important
   applications so as to minimize energy consumption.  Examples include
   retrieval of content or remote computation.  This allows to minimize
   the amount of communication that needs to take place in the first
   place, although energy savings within the network may at least in
   part be offset by additional energy consumption elsewhere.  The
   following are some examples that suggest that it may be worthwhile
   reconsidering 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], studied as well in
   the IRTF ICNRG.  This however shifts the energy consumption back to
   the network operator and requires some power-hungy hardware, such as
   chips for larger name look-ups 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 computation in a an energy-optimized

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   data center at very large scale, but requiring transmission of
   significant volumes of data across many nodes and long distances,
   versus performing computational tasks at the edge where the energy
   may not be used as efficiently (less multiplexing of resources, and
   smaller sites are inherently less efficient due to their smaller
   scale) but the amount of long-distance network traffic is
   significantly reduced.  Softwarization, containers, microservices are
   direct enablers for such architectures, and the deployment of
   programmable network infrastructure (as for instance Infrastructure
   Processing Units - IPUs or smartNICs that offload some computations
   from the CPU onto the NIC) will help its realization.  However, the
   power consumption characteristics of CPUs are different from those of
   NPUs, another aspect to be considered in conjunction with

   Other possibilities concern taking economic aspects into
   consideration impact, such as providing incentives to users of
   networking services in order to minimize energy consumption and
   emission impact.  An example for this is given in
   [wolf2014choicenet], which could be expanded to include energy

   Other approaches consider performing a late binding of data and
   functions to be performed on the data [krol2017NFaaS].  The COIN
   Research Group in IRTF focuses on similar issues.  Jointly optimizing
   for the total energy cost, taking into account networking and
   computing (and the different energy cost of computing in an
   hyperscale DC vs an edge node) is still an area of open research.

   In summary, rethinking of 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 extend 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 that can
   provide the basis for IETF-led advances in this space:

   *  Investigate organization of networking architecture for important
      classes of applications (examples: content delivery, right-placing
      of computational intelligence, industrial operations and control,
      massively distributed machine learning and AI) to optimize green
      foot print and holistic approaches to trade off carbon footprint
      between forwarding, storage, and computation.

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   *  Models to assess and compare alternatives in providing networked
      services, e.g. assess carbon impact relative to alternatives where
      as to where to perform compute, 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 concerns
   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 allow to assess the
   energy impact of decisiont taken.  It will also help to answer
   questions such as: is caching - with the associated storage energy -
   better than retransmitting from a different server - with the
   associated networking cost?  Is compression more energy-efficient
   once factoring 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 by ways of better decisions that
   this data enables?  Is transmitting data to a Low Earth Orbit (LEO)
   satellite constellation compensated by the fact that once in the
   constellation, the networking is fueled on solar energy?  Is the
   energy cost of sending rockets to place routers in Low Earth Orbit
   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 IETF has an important role to play in facilitating this.

9.  IANA Considerations

   This document does not have any IANA requests.

10.  Security Considerations

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

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   Security vulnerabilities of networks may manifest themselves in
   compromised energy efficiency.  For example, attackers could aim at
   increasing energy consumption in order to drive up attack victims'
   energy bill.  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 tradeoffs between security and
   maximal energy efficiency that might otherwise be achieved.  An
   example is encryption, which requires additional computation for
   encryption and decyption 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 to consider in protocol designs or in
   measurements is susceptible to create such covert or side channel and
   this should be taken into account while designing energy efficient

11.  Contributors

      Michael Welzl, University of Oslo,

12.  Acknowledgments

   We thank Dave Oran for providing the information regarding covert
   channels using energy measurements.  Additional acknowledgments will
   be added at a later stage.

13.  Informative References

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              Alizadeh, M., Greenberg, A., Maltz, D., Padhye, J., Patel,
              P., Prabhakar, B., Sengupta, S., and M. Sridharan, "Data
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              Bolla, R., Bruschi, R., Davoli, F., and F. Cucchietti,
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              Chabarek, J., Sommers, J., Barford, P., Tsiang, D., and S.
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   [DRAM]     Paiva, T. B., Navaridas, J., and R. Terada, "Robust Covert
              Channels Based on DRAM Power Consumption", In book:
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   [emergy]   Raghavan, B. and J. Ma, "The Energy and Emergy of the
              Internet", ACM HotNets , 2011.

              Faber, G., "A framework to estimate emissions from virtual
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              a Junkyard Datacenter", arXiv:2110.06870v1, October 2021 ,

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   [QUAL]     Li, R., Makhijani, K., Yousefi, H., Westphal, C., Xong,
              L., Wauters, T., and F. D. Turck, "A framework for
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   [RFC2481]  Ramakrishnan, K. and S. Floyd, "A Proposal to add Explicit
              Congestion Notification (ECN) to IP", RFC 2481,
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   [RFC3031]  Rosen, E., Viswanathan, A., and R. Callon, "Multiprotocol
              Label Switching Architecture", RFC 3031,
              DOI 10.17487/RFC3031, January 2001,

   [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.,
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              Compression (ROHC): Framework and four profiles: RTP, UDP,
              ESP, and uncompressed", RFC 3095, DOI 10.17487/RFC3095,
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   [RFC7950]  Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
              RFC 7950, DOI 10.17487/RFC7950, August 2016,

   [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,
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Authors' Addresses

   Alexander Clemm
   2330 Central Expressway
   Santa Clara,,  CA 95050
   United States of America

   Cedric Westphal

   Jeff Tantsura

   Laurent Ciavaglia

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Internet-Draft               Chall-Ops-Green                  March 2023


   Marie-Paule Odini

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