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Challenges and Opportunities in Green Networking
draft-cx-green-ps-01

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This is an older version of an Internet-Draft whose latest revision state is "Replaced".
Authors Alexander Clemm , Cedric Westphal , Jeff Tantsura , Laurent Ciavaglia , Marie-Paule Odini
Last updated 2022-10-20
Replaced by draft-irtf-nmrg-green-ps
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draft-cx-green-ps-01
Network Working Group                                           A. Clemm
Internet-Draft                                               C. Westphal
Intended status: Informational                                 Futurewei
Expires: 23 April 2023                                       J. Tantsura
                                                               Microsoft
                                                            L. Ciavaglia
                                                                   Nokia
                                                              M-P. Odini
                                                         20 October 2022

            Challenges and Opportunities in Green Networking
                          draft-cx-green-ps-01

Abstract

   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.  The biggest opportunities to reduce the energy footprint
   may not be networking specific, for instance general power efficiency
   gains in hardware or hosting of equipment in more cooling-efficient
   buildings.  Yet methods to make networking technology itself
   "greener" also need to be explored.  This document outlines a
   corresponding set of opportunities, along with associated research
   challenges, for reducing this footprint and reducing network energy
   demand.

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
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 23 April 2023.

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Copyright Notice

   Copyright (c) 2022 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
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components
   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   6
   3.  Contributors to Network Energy Consumption  . . . . . . . . .   6
   4.  Challenges and Opportunities - Equipment Level  . . . . . . .   7
   5.  Challenges and Opportunities - Protocol Level . . . . . . . .   9
     5.1.  Data Volume Reduction . . . . . . . . . . . . . . . . . .   9
     5.2.  Protocol Optimization . . . . . . . . . . . . . . . . . .  10
     5.3.  Enabling Network Energy Saving Mechanisms . . . . . . . .  11
     5.4.  Network Addressing  . . . . . . . . . . . . . . . . . . .  12
   6.  Challenges and Opportunities - Network Level  . . . . . . . .  12
   7.  Challenges and Opportunities - Architecture Level . . . . . .  14
   8.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  15
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  17
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  17
   11. Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  17
   12. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  18
   13. Informative References (TBD)  . . . . . . . . . . . . . . . .  18
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  21

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

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   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
   https://www.ietf.org/blog/towards-a-net-zero-ietf/).  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, Telefonica reports that in 2020,
   its network's energy consumption per PB of data amounted to 78MWh
   [telefonica2020].  This rate has has been dramatically decreasing (a
   five-fold factor over five 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 reduce overall carbon emissions by 70% over the
   next 5 years.

   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.  For one, the sustainability
   of power sources need to be taken into account.  A deployment that
   includes devices that are less energy-efficient but that are powered

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   by a sustainable energy source can arguably be considered "greener"
   than a deployment that includes highly-efficient device powered by
   Diesel generators.  In fact, in the same Telefonica report, extensive
   reliance on renewable energy sources is emphasized.  Similar,
   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.
   Extending the lifetime of equipment may in many cases be preferable
   over replacing it earlier with slightly more energy-efficient.

   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 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,
      enabling (for example) controllers 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 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

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      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.

   *  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 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 power consumption 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
      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.

   It should be noted that this document borrows to a fair extent
   material from a prior paper, [GreenNet22].  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, unlike the prior paper, this document focuses on
   and attempts to articulate specific challenges as related to work
   that could be championed by the IETF.

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2.  Definitions and Acronyms

   TBD

3.  Contributors to Network Energy Consumption

   When exploring possibilities to improve energy efficiency, it is
   important to understand which aspects contribute to power consumption
   the most and hence where the greatest potential for power savings
   lies.

   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
   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 the first bit is very high, as it requires
   powering up a device, port, etc.  The 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.

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   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,
   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

   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

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   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.

   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].

   Beyond such "first-order" opportunities, 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 would 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, no corresponding standardized YANG data models [RFC7950] for
   network energy consumption that can be used in conjunction with
   management and control protocols have been defined.

   Instrumentation should also take into account the possibility of
   virtualization, introducing layers of indirection to assess the
   actual energy usage.  For example, virtualized networking functions
   could be hosted on containers or virtual machines which are hosted on
   a CPU in a data center instead of a regular network appliance such as

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   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
   equipment.

5.  Challenges and Opportunities - Protocol Level

   There are several opportunities for energy savings at the protocol
   level.  We characterize them along three main categories: protocols
   designed to reduce the volume of data to be transmitted; protocols
   designed to optimize data transmission rates under energy
   considerations; and protocols that enable energy optimization schemes
   at the network level.  A fourth category, "other", is used to capture
   any other aspects not easily categorized into the other three.

5.1.  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
   necessary.

   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

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   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).

   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.

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 minimial link utilization) and attempt to turn off some
   resources to save power.

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   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.

5.3.  Enabling Network Energy Saving Mechanisms

   Novel protocols are also needed in two dimensions: to discover what
   links are available and/or energy efficient.  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 resources.

   Also, protocols are required to quickly converge onto an energy-
   efficient path once a new topology is created by turning links on/
   off.  Current routing protocols may provide for fast recovery in the
   case of failure.  However, failures are hopefully relatively rare
   events, while we expect an energy efficient network to aggressively
   try to turn off links.

   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.

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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.

6.  Challenges and Opportunities - Network Level

   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 energy usage 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 energy footprint as a cost factor.  Low-hanging fruit
   include the inclusion of energy-related parameters as a cost
   parameter in control planes, whether distributed (e.g.  IGP) or
   conceptually centralized via SDN controllers.

   Other opportunities concern adding energy-awareness to dynamic path
   selection schemes, requiring corresponding instrumentation as
   mentioned earlier.  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

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   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.

   As an important prerequisite to capture many of those opportunities,
   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 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.

   A related set of challenges concerns the fact that such 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, extensive
   recalculation e.g. of routes and paths based on the current energy
   state of the network needs to be avoided.

   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

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   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.

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
   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

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   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
   virtualization.

   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
   incentives.

   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.

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
   some of the technical challenges and opportunities in that regard,
   for example:

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

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

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   *  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;

   *  Network advances to allow to dynamically take resources offline
      where feasible while minimizing churn;

   *  Energy footprint aware traffic steering and routing; carbon
      footprint as a traffic cost metric to optimize;

   *  Reorganization of networking architecture for important classes of
      applications (examples: content delivery, right-placing of
      computational intelligence) to optimize green foot print and
      holistic approaches to trade off carbon footprint between
      forwarding, storage, and computation;

   *  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;

   *  Reliability issues for a network that relies on fewer resource
      diversity, and with more operational complexity.

   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 LEO 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.

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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
   caveats.

   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
   protocols.

11.  Contributors

      Michael Welzl, University of Oslo, michawe@ifi.uio.no

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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 (TBD)

   [ahlgren2012survey]
              Ahlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D.,
              and B. Ohlman, "A survey of information-centric
              networking", IEEE Communications Magazine Vol.50 No.7,
              2012.

   [alizadeh2010DCTCP]
              Alizadeh, M., Greenberg, A., Maltz, D., Padhye, J., Patel,
              P., Prabhakar, B., Sengupta, S., and M. Sridharan, "Data
              Center TCP (DCTCP)", ACM SIGCOMM pp.63-74, 2010.

   [bianco2016energy]
              Bianco, A., Mashayekhi, R., and M. Meo, "Energy
              consumption for data distribution in content delivery
              networks", IEEE International Conference on Communications
              (ICC) pp.1-6, 2016.

   [bolla2011energy]
              Bolla, R., Bruschi, R., Davoli, F., and F. Cucchietti,
              "Energy Efficiency in the Future Internet: A Survey of
              Existing Approaches and Trends in Energy-Aware Fixed
              Network Infrastructures", IEEE Communications Surveys and
              Tutorials Vol.13 No.2, pp.223-244, 2011.

   [cervero19]
              Cervero, A. G., Chincoli, M., Dittmann, L., Fischer, A.,
              and A. Garcia, "Green Wired Networks", Wiley Journal on
              Large-Scale Distributed Systems and Energy
              Efficiency pp.41-80, 2019.

   [chabarek08]
              Chabarek, J., Sommers, J., Barford, P., Tsiang, D., and S.
              Wright, "Power awareness in network design and routing",
              IEEE Infocom pp.457-465, 2008.

   [DRAM]     Paiva, T. B., Navaridas, J., and R. Terada, "Robust Covert
              Channels Based on DRAM Power Consumption", In book:
              Information Security (pp.319-338) , 2019.

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   [emergy]   Raghavan, B. and J. Ma, "The Energy and Emergy of the
              Internet", ACM HotNets , 2011.

   [framework]
              Faber, G., "A framework to estimate emissions from virtual
              conferences", International Journal of Environmental
              Studies, 78:4, 608-623 , 2021.

   [GreenNet22]
              Clemm, A. and C. Westphal, "Challenges and Opportunities
              in Green Networking", 1st International Workshop on
              Network Energy Efficiency in the Softwarization Era IEEE
              NetSoft 2022, June 2022.

   [herzen2011PIE]
              Herzen, J., Westphal, C., and P. Thiran, "Scalable routing
              easy as PIE: A practical isometric embedding protocol",
              19th IEEE International Conference on Network Protocols
              (ICNP) pp.49-58, 2011.

   [I-D.ietf-tsvwg-l4s-arch]
              Briscoe, B., Schepper, K. D., Bagnulo, M., and G. White,
              "Low Latency, Low Loss, Scalable Throughput (L4S) Internet
              Service: Architecture", Work in Progress, Internet-Draft,
              draft-ietf-tsvwg-l4s-arch-20, 29 August 2022,
              <https://www.ietf.org/archive/id/draft-ietf-tsvwg-l4s-
              arch-20.txt>.

   [islam2012evaluating]
              Islam, S. U. and J. Pierson, "Evaluating Energy
              Consumption in CDN Servers", Proceedings of the Second
              International Conference on ICT as Key Technology against
              Global Warming pp.64-78, 2012.

   [junkyard] Switzer, J., Kastner, R., and P. Pannuto, "Architecture of
              a Junkyard Datacenter", arXiv:2110.06870v1, October 2021 ,
              2021.

   [krol2017NFaaS]
              Krol, M. and I. Psaras, "NFaaS: Named Function as a
              Service", ACM SIGCOMM ICN Conference , 2017.

   [mathew2011energy]
              Mathew, V., Sitaraman, R., and P. Shenoy, "Energy-Aware
              Load Balancing in Content Delivery Networks", CoRR
              http://arxiv.org/abs/1109.5641 , 2011.

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   [NewClass] Khatamifard, S. K., Wang, L., Kose, S., and U. R.
              Karpuzcu, "A New Class of Covert Channels Exploiting Power
              Management Vulnerabilities", IEEE Computer Architecture
              Letters , 2018.

   [QUAL]     Li, R., Makhijani, K., Yousefi, H., Westphal, C., Xong,
              L., Wauters, T., and F. D. Turck, "A framework for
              Qualitative Communications using Big Packet Protocol",
              Proceedings ACM Sigcomm Workshop On Networking For
              Emerging Applications And Technologies pp.22-28, 2019.

   [ren2018jordan]
              Ren, J., Ren, K., Westphal, C., Wang, J., Wang, J., Song,
              T., Liu, S., and J. Wang, "JORDAN: A Novel Traffic
              Engineering Algorithm for Dynamic Adaptive Streaming over
              HTTP", IEEE International Conference on Computing,
              Networking and Communications (ICNC) pp.581-587, 2018.

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

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

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

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

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

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   [SideChannel]
              Randolph, M. and W. Diehl, "Power Side-Channel Attack
              Analysis: A Review of 20 Years of Study for the Layman",
              Cryptography 2020, 4, 15 , 2020.

   [TCC]      Rahimi, P., Singh, A. K., and X. Wang, "Selective Noise
              Based Power Efficient and Effective Countermeasure Against
              Thermal Covert Channel Attacks in Multi-Core Systems",
              Journal on Low Power Electronics and Applications , 2022.

   [telefonica2020]
              Telefonica, "Consolidated Management Report 2020", 2021.

   [TradeOff] Welzl, M., "Not a Trade-Off: On the Wi-Fi Energy
              Efficiency of Effective Internet Congestion Control",
              IEEE/IFIP WONS , 2022.

   [westphal2021qualitative]
              Westphal, C., He, D., Makhijani, K., and R. Li,
              "Qualitative Communications for Augmented Reality and
              Virtual Reality", 22nd IEEE International Conference on
              High Performance Switching and Routing (HPSR) pp.1-6,
              2021.

   [wolf2014choicenet]
              Tilman, W., Griffioen, J., Calvert, L., Dutta, R.,
              Rouskas, G., Baldin, I., and A. Nagurney, "ChoiceNet:
              Toward an Economy Plane for the Internet", SIGCOMM
              Computer Communciations Review Vol.44 No.3, July 2014.

Authors' Addresses

   Alexander Clemm
   Futurewei
   2330 Central Expressway
   Santa Clara,,  CA 95050
   United States of America
   Email: ludwig@clemm.org

   Cedric Westphal
   Futurewei
   Email: cedric.westphal@futurewei.com

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

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

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

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