Network Working Group M. Welzl
Internet-Draft University of Oslo
Intended status: Informational E. Stephan
Expires: 4 September 2025 Orange
E. Schooler
University of Oxford
S. Rumley
HES-SO
A. Rezaki
Nokia
J. Manner
Aalto University
C. Pignataro
Blue Fern Consulting
M. Palmero
Cisco
J. Lindblad
All For Eco
S. Krishnan
Cisco
A. Keränen
Ericsson
H. ElBakoury
L. M. Contreras
Telefonica
A. Clemm
Independent
J. Arkko
Ericsson
3 March 2025
Architectural Considerations for Environmentally Sustainable Internet
Technology
draft-various-eimpact-arch-considerations-00
Abstract
This document discusses protocol and network architecture aspects
that may have an impact on the sustainability of network technology.
The focus is on providing guidelines that can be helpful for protocol
designers and network architects, where such guidelines can be given.
About This Document
This note is to be removed before publishing as an RFC.
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The latest revision of this draft can be found at
https://jariarkko.github.io/draft-eimpact-arch-considerations/draft-
eimpact-arch-considerations.html. Status information for this
document may be found at https://datatracker.ietf.org/doc/draft-
various-eimpact-arch-considerations/.
Source for this draft and an issue tracker can be found at
https://github.com/jariarkko/draft-eimpact-arch-considerations.
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This Internet-Draft will expire on 4 September 2025.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Potential Architectural Aspects . . . . . . . . . . . . . . . 5
2.1. Measurement . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1. Motivation . . . . . . . . . . . . . . . . . . . . . 6
2.1.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 6
2.1.3. Recommendation . . . . . . . . . . . . . . . . . . . 7
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2.2. Modeling . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1. Motivation . . . . . . . . . . . . . . . . . . . . . 9
2.2.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 9
2.2.3. Recommendation . . . . . . . . . . . . . . . . . . . 11
2.3. Dynamic Scaling . . . . . . . . . . . . . . . . . . . . . 11
2.3.1. Motivation . . . . . . . . . . . . . . . . . . . . . 12
2.3.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3. Recommendation . . . . . . . . . . . . . . . . . . . 15
2.4. Transport . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.1. Motivation . . . . . . . . . . . . . . . . . . . . . 15
2.4.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 16
2.4.3. Recommendation . . . . . . . . . . . . . . . . . . . 17
2.5. Equipment Longevity . . . . . . . . . . . . . . . . . . . 17
2.5.1. Motivation . . . . . . . . . . . . . . . . . . . . . 17
2.5.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 18
2.5.3. Recommendation . . . . . . . . . . . . . . . . . . . 19
2.6. Compact encoding . . . . . . . . . . . . . . . . . . . . 19
2.6.1. Motivation . . . . . . . . . . . . . . . . . . . . . 19
2.6.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 19
2.6.3. Recommendation . . . . . . . . . . . . . . . . . . . 20
2.7. Sustainable by Design: Data Governance Perspective . . . 20
2.7.1. Motivation . . . . . . . . . . . . . . . . . . . . . 20
2.7.2. Analysis . . . . . . . . . . . . . . . . . . . . . . 20
2.7.3. Recommendation . . . . . . . . . . . . . . . . . . . 21
3. Recommendations for Further Work and Research . . . . . . . . 21
4. Security Considerations . . . . . . . . . . . . . . . . . . . 22
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 22
6. Informative References . . . . . . . . . . . . . . . . . . . 22
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 26
1. Introduction
Environmental sustainability is an important consideration in
networking. Both for ensuring that networking technology can enable
societies to operate in an environmentally sustainable manner and
that the networks themselves are environmentally sustainable.
This document discusses protocol and network architecture aspects
that may have an impact on the environmental sustainability of
network technology. For brevity, we will use the term sustainability
to refer to environmental sustainability. We do note that
sustainability as a term is widely used to refer to different notions
of sustainability, and the most well-known larger definition of
sustainability can be seen from the United Nations Sustainable
Development Goals (UN SDG) [UNSDG].
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Sustainability impact and emissions from networking comes from three
primary categories: hardware manufacturing, direct energy usage and
construction work. The last category is out of scope of this
document because networking has limited means to impact construction
work itself. The manufacturing of networking hardware, both for
fixed and wireless networks, is a significant source of emissions,
and recycling of ICT equipment is still limited. Direct energy usage
of networking and the source of the energy have been the primary
concerns, but as the world moves towards greener energy production,
the relative negative impact of the emissions from manufacturing
becomes more prominent.
When good design and architecture can improve the sustainability of
networks, they should certainly be applied to designing new protocols
and building networks. Intuitively, protocol and network
architecture choices can have an impact on sustainability. At the
very least the right design and architecture can make it possible to
have a positive impact, but of course the architecture alone is not
enough. The possibilities offered by the architecture need to be
realized by implementations and practical deployments.
To give an example of an architectural aspect that potentially has a
sustainability impact, enabling the collection of information (e.g.,
energy consumption) and then using that information to make smarter
decisions is one. For instance, understanding power consumption of
individual nodes can be valuable input to future purchasing decisions
or development efforts to reduce the power consumption. Yet, as data
collection is often rather easy, we should not overdo it in such a
way that it leads to accumulation of dark data (i.e. data that is
collected and stored, but never used). All data collection consumes
processing power, network resources and storage space, and this can
in turn increase the emissions from the network.
Other architectural examples include making it possible to scale
resources or resource selection processes performed in a
sustainability-aware fashion. The use of communication primitives
that maximize utility in a given problem (e.g., using multicast) or
the use of technologies that reduce the number or size of messages
needed for a given task (e.g., binary encoding instead of textual)
are some further examples.
Of course, some of these aspects may have a major impact on
sustainability, where others may only have a minor effect. There are
also tradeoffs, such as side-effects of architectural choices, e.g.,
dynamic scaling of a router network potentially impacting jitter, or
putting cellular base stations to sleep and activating them as
capacity needs grow may introduce a delay in matching the needs of
the data flows.
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The document is intended to help engineering efforts in the IETF,
provide operational guidance in the operator community, as well as to
point to potential research directions in the IRTF.
The scope of the document is advice on Internet and protocol
architecture, such as what architecture or capabilities new protocol
designs or features should have, what kind of operational network
architectures should be deployed, and how all of these can be
designed to best address sustainability concerns. The focus of this
document is to provide actionable design advice to protocol
designers. This document therefore addresses one aspect in the
architecture question, and does not claim to cover the topic
exhaustively.
This document is also not focused on general issues around
environmental sustainability, except those that pertain to
architecture or significant protocol features.
It is to be noted that networks themselves are a service, a tool, for
all the applications and services on the Internet. Networks connect
data, people and services. The increase in networking and size of
the Internet is driven by these applications and the usage.
Therefore the emissions from networking are tied to the applications
and the data they consume; with less applications or data, the
Internet would have less hardware and less energy usage. The goals
of this document are not to instruct application and service
developers to choose what applications are worthwhile or how much
content is sent. There are many forums and parties whose mission is
to help these developers to implement more sustainable services, such
as, the Green Software Foundation, the Green Web Foundation, Greening
of Streaming, to name a few.
2. Potential Architectural Aspects
This section presents architectural and protocol design aspects that
can have an impact on the sustainability of networking. For each
topic, we provide an overview, the motivation for why it would be
important to consider for more sustainable networking, an analysis
and recommendations for future networking professionals.
2.1. Measurement
It is essential to understand the current state of affairs before any
improvements can be made. i.e. Some levels of measurements are
necessary for starting to improve sustainability. This is
particularly the case when looking at the systems as a whole in post-
analysis. As discussed earlier, this level of measurements is useful
input for further actions, such as deciding what parts of the network
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need to be targeted for further improvement.
But measurements may also be useful for some dynamic situations where
power-saving decisions, for instance, depend on knowing the relative
power consumption of different activities, such as when a power-off
decision involves understanding the relative savings during the
shutdown period vs. the power cost of shutdown and startup
procedures, or the possible need to reconfigure other nodes in the
network due to the shutdown.
2.1.1. Motivation
Measurements are a necessary mechanism for both post-analysis and
potentially for some of the dynamic decisions taken by systems.
Without measurements of any kind, it is impossible to assess if the
networks are functioning correctly. It is impossible to know if the
system is efficient by comparing it against a baseline model. It is
also impossible to check that changes aiming at optimizing something
are indeed valuable.
For instance, while electricity providers can make information about
power usage available, this is only done at the aggregate level.
Without per-device data about power usage, there would be limited
basis for deciding where power is actually consumed and consequently,
what improvements are most useful.
At the same time, it is not possible to measure everything.
Furthermore, any measurement must be validated. Relevance of
measurements must be periodically assessed, e.g., with comparisons
between measurements within a network and the aggregate numbers from
the electricity provider.
Finally, measurements made in the field must be collected and
organized to allow later retrieval.
2.1.2. Analysis
While the simplest forms of sustainability-related measurements are
about power, there's clearly room for other measurements and other
information as well. To begin with, power consumption by itself may
not be what matters most for sustainability, as the source of the
power may be equally important in terms of determining the actual
carbon footprint.
Secondly, for many classes of devices the embedded carbon aspects or
use of raw materials may be a significant sustainability issue. See
also Section 2.2.
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Third, power or energy measurements alone are of meager use if the
cause of the consumption is not measured as well. Any power/energy
measurement should occur alongside other measurements that can be
used to determine energy efficiency. Hence a sound measurement
architecture implies that a prior existence of an energy efficiency
framework of some kind.
But when it comes to energy consumption, as noted the aggregate
information is often typically available, and it's not particularly
hard to measure the energy consumption of individual network devices
either. Still, there are a number of desirable use cases where the
measurement situation needs to improve.
2.1.2.1. Measuring Power Efficiency
When assessing the power consumption (Scope 2) of an IT-organization,
emission accountants are generally looking for a metric of the
delivered value per unit of energy.
A commonly used method is to equate the delivered value with the
number of bits sent or received, or to the communication capacity
made available when there's a need for it. The latter is important,
as often communication networks have requirements to be able to send
messages when there's a need for it, e.g., for emergency
communications, not that those messages are always being sent.
2.1.3. Recommendation
Ongoing work at the IETF's GREEN working group is already targeted at
improving existing energy consumption metrics and frameworks but some
further considerations may apply. In order to meet the needs
discussed above, the following architectural design principles are
proposed.
2.1.3.1. Generality
We recommend that any measurement framework or sustainability-related
information sharing mechanism be designed to share different types of
information and not limited to a single metric such as power
consumption. Similarly, the granularity of data collection needs to
be configurable so that the metrics collected can be as fine-grained
or as aggregated as needed in order to identify potential areas of
improvement.
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2.1.3.2. Collect Metrics from Existing Equipment
Since the need to deliver on the use cases described is urgent, the
industry has to accomodate the capabilities (and limitations) of
existing equipment in the field for collecting metrics.
It is recommended to have a plug-in architecture with modules that
can work with (read from and control) devices of any kind, including
traditional networking hardware devices, cooling systems, software
stacks, and occasionally static datasheets.
2.1.3.3. Content Declaration for all Collected Metrics
A warehouse filled with data collected from diverse sources is
useless without proper labeling. Hence, these is a need to create
metadata that describes the collected data. (e.g. What are the
source(s)? What measurement units are used? Precision? What is
included/excluded in these numbers?)
The metadata itself must also have a formal description. e.g. Use
YANG for the metadata schema. Keep the metadata attached to the
dataflow it describes, so that the relation is clear to each
component that has anything to do with it, including components added
by other organizations at a later point in time.
2.1.3.4. Collection, Aggregation, Processing, Display, Decisions
The collected data passes through a pipeline from collection to
decisions. By processing we mean steps to reshape the data to match
further aggregation and processing steps, such as unit conversions,
sample frequency alignment, filtering, etc.
Separate these architectural roles into separate modules in order to
enable reuse, modular development and a transparent, configurable
pipeline.
2.1.3.5. Configurable Pipeline for Reuse and Transparency
Let the pipeline connections between the components be driven by
configuration rather than hard coded. This enables reconfiguration
of the processing pipeline over time, and perhaps more importantly,
transparency into what stages the data pass through, even without
access to or understanding of the source code of the entire system.
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2.1.3.6. Design Together with the Users
Every system should be designed involving some of its target users.
In order for delivered metrics to be of any value, the target
audience needs to be aware of their existence, be able to interpret
them and understand how they can be used in their professional
context.
There are many target user groups for the information produced. Some
examples are network designers/engineers, scientists, operations
teams and IT-development organizations. One critical group that is
often overlooked is the sustainability assessment experts. If they
are not aware, don't understand or don't care about the produced
sustainability metrics, the value of this work would be greatly
diminished.
2.2. Modeling
The paucity of up-to-date information on equipment and system
parameters, especially power consumption and maximum throughput,
makes estimating the power consumption and energy efficiency of these
systems extremely challenging. In addition the rapid evolution of
technology and products in ICT makes the estimation quickly outdated
and possibly inaccurate. In almost all cases physical measurement
has to be replaced by partial measurement and mathematical modeling.
2.2.1. Motivation
Where power optimization choices are made, accurate information is
required to decide the right choice. Modeling instead of
measurements may have to be used in some cases.
2.2.2. Analysis
To date, two approaches to network power modeling are accepted as
providing a realistic estimate of network power consumption. These
approaches are referred to as "bottom-up" and "top-down". The paper
[Unifying] surveys both approaches and provide a new approach which
unifies both of them. The unified approach is used to estimate the
power consumption of access, aggregation and core networks.
The paper [Modeling] provides a model for IP Routers and the routers
of other future Internet architectures (FIA) such as SCION and
NEBULA. They use a generic model which captures the commonalities of
IP router as well as the peculiarities of FIA routers. They conduct
a large-scale simulation based on this router model to estimate the
power consumption for different network architectures.
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Since routers and other network devices and functions can be
virtualized, this article (1) provides comprehensive "graphical,
analytical survey of the literature, over the period 2010–2020, on
the measurement of power consumption and relevant power models of
virtual entities as they apply to the telco cloud." This paper A
Methodology and Testbed to Develop an Energy Model for 5G Virtualized
RANs IEEE Conference Publication IEEE Xplore got best paper award for
GreenNet 2024, but I am not sure if we are interested to model 5G
vRAN.
There is a plethora of publications on modeling communication
networks and DC computing.
2.2.2.1. Customer Attribution
When organizations assess their Scope 3 emissions, they need to sum
up their share of emissions from all their suppliers, one of which
for example, might be a cloud hosting service. In order for the
supplier to provide an emission share value back to the customer, the
provider needs to develop a mechanism for attribution.
A significant challenge in accurately assessing Scope 3 emissions is
avoiding Double Counting, where the same emission is reported by
multiple entities. According to the GHG Protocol best practices, it
is crucial to establish clear guidelines and agreements between
suppliers and customers to ensure that emissions are attributed
correctly and not counted multiple times. This requires transparent
communication and precise emission reporting standards to ensure that
all parties involved have a consistent understanding of which
emissions belong to which organization.
By addressing the Double Counting issue, companies can achieve more
accurate and reliable Scope 3 emissions assessments, thereby
contributing to better overall sustainability reporting and
improvement efforts.
2.2.2.2. Baselining and Benchmarking
Establishing a baseline is a fundamental step in the process of
improving energy efficiency and sustainability of network technology.
Baselining involves establishing a reference point of typical energy
usage, which is crucial for identifying inefficiencies and measuring
improvements over time. In this step, the controller uses only the
collected data from datasheets and other reliable sources.
By establishing a baseline and using benchmarking, organizations can
determine if their networking equipment is performing normally or if
it is deviating from expected performance. This is the first step in
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identifying and guiding necessary improvements. Benchmarking
involves collecting performance measurements of networking equipment
under controlled conditions. This process helps establish
standardized performance metrics, allowing for comparison against
baselines collected during regular operational conditions.
The initial measurement of networking equipment's energy efficiency
and performance, known as Baselining, should be coordinated with
vendor specifications and industry standards to understand what is
considered normal or optimal performance. For example, if the
baseline indicates that your switches operate at 5 Gbps per watt,
while vendor specifications suggest 8 Gbps per watt and the industry
standard is 10 Gbps per watt, actions should be taken to implement
energy-saving measures and upgrades. Continuously tracking
subsequent measurements can reveal if efficiency improves towards the
benchmark of 8-10 Gbps per watt.
This practice ensures that any improvements can be quantifiably
tracked over time, providing a clear measure of the effectiveness of
the implemented changes and guiding further enhancements in network
sustainability.
See also [Baseline] and [BenchmarkingFramework].
2.2.3. Recommendation
Even though baselining is essential in identifying potential areas of
improvement and tracking progress, it is still to be determined to
what extent we need to work on modeling networks and devices in the
architecture.
2.3. Dynamic Scaling
Dynamic scaling is the ability to adjust resources according to
demand, and possibly turn some of them off during periods of low
usage. Examples include the set of servers needed for a service, how
many duplicate links are needed to carry high-volume traffic, whether
one needs all base stations with overlapping coverage areas to be on,
etc.
Networks and communications are also critical functions of the modern
digital society. The reliability of individual networking links or
devices cannot always be guaranteed. As a result, various levels and
forms of resiliency are often needed, for instance through
redundancy. Yet, there is a question on how much redundancy is
needed and how quickly a backup or resource increase can be activated
due to increased demand.
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2.3.1. Motivation
Outside of implementation improvements, dynamic scaling is
potentially the most promising method for reducing power consumption
related environmental impacts. Scaling can happen on a device-level
(increasing performance as traffic levels grow) or a network segment
level (increasing the number of active links or cellular base
stations).
Considering current fixed networking hardware, dynamic scaling might
not have an impact in situations where there's only a single router
or server serving a particular route, area, or function. Current
routers and switches exhibit limited potential dynamic scaling
because the focus is on high performance and a stable connectivity.
There have been some recent improvements on this front as well. e.g.
Energy-Efficient Ethernet (EEE) is a good example of a networking-
level specification to lower energy consumption in idle mode. EEE
has limited impact on a network that has continuous traffic.
Resiliency can be implemented within a single router as well, e.g. as
a backup power supply, between routers and switches as multiple links
between the same nodes, having different links between two end
points, overlapping cellular coverage, etc. All these necessarily
add more hardware to provide the same exact service. Some of that
hardware can be fully operational at all times and used to serve the
traffic, while other links may be in hot or cold standby depending on
the use case.
Cellular networks are typically built with significant overlap in
coverage areas of multiple base stations. Demand and business
reasons dictate the design of the coverage, and regulations might
dictate how reliable the cellular service should be. There is
extensive work world-wide to optimize the operation of this
overlapping coverage, e.g. by turning down some sites at night time
when traffic volumes are low. A cellular basestation site can
consume anything from a few kWh to ten or more kWh per provider.
Modern cellular base stations do implement numerous features to scale
the energy consumption. In general, cellular base stations have a
base energy consumption and traffic-dependent consumption, a somewhat
similar behavior to what we can observe in modern CPUs.
On the network level, most large systems have significant amount of
redundancy and spare capacity. Where such capacity can be turned on
or off to match the actual need at a given time, significant
reductions in power consumption can be achieved.
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2.3.2. Analysis
Dynamic scaling could be seen as either an alternative or
complementary to load stabilization, e.g., via "peak shaving".
Perhaps the most realistic angle is that both are likely needed.
The most rudimentary approach to dynamic scaling is just turning some
resources off. However this may not be sufficient, and a more
graceful/engineered approach potentially yields better results.
Network architects need to understand the impacts of scaling changes
on users and traffic. These may include the fate of ongoing
sessions, latency/jitter, packets in flight, or running processes,
attempts to contact resources that are no longer present, and the
time it takes for the network to converge to its new state.
Dynamic scaling requires an understanding of load levels for the
network, so information collection is required. It also requires
understanding the power, time and other costs of making changes.
(See [I-D.pignataro-enviro-sustainability-architecture] for
discussion of tradeoffs and multi-objective optimization.)
Understanding the resiliency requirements for a network or a piece of
equipment is also important for the optimal control of resiliency,
e.g., as an input to decisions on how many instances of replicated
services need to be run and where.
Some of the strategies that are useful in implementing a well working
dynamic scaling include:
* Matching the currently used resources to the actual need, be it
about traffic demand or resiliency. One way to do this is to use
of power-proportional underlying technologies, such as chipsets or
transmission technologies. And where this is not sufficient, the
ability to turn components/systems on and off is an alternative
strategy.
* Using load adaptive techniques allows the capacity of the nodes to
be dynamically adjusted according to the demand. Examples include
Adaptive Link Rate (ALR), which dynamically adapts the link rate
to suit traffic demand or power off links in Link Aggregation
based on traffic demand which is empirically estimated based on
traffic arrival. LACP (Link Aggregation Control Protocol) defined
in IEEE 802.1AX [LinkAggregation] can be modified to power off
links in an aggregation if they are not needed.
* Ability to enter "no new work" mode for equipment, to enable some
resources to be eventually released/turned off.
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* Ability to move ongoing tasks off to other equipment, to prevent
disruption of already started tasks.
* Ability to schedule changes in advance rather than making them
abruptly, with associated signaling exchanges and possible
transient routing and other failures. See for instance the time-
variant routing work in the IETF [RFC9657]
[I-D.ietf-tvr-requirements] [I-D.ietf-tvr-schedule-yang]
[I-D.ietf-tvr-alto-exposure].
* Efficient propagation of changes of new routes, new set of
servers, etc. as to reduce the amount of time where state is not
synchronized across the network. The needs for the propagation
solution needs to be driven by dynamic scaling and sustainability
as well as other aspects, such as recovery from failures.
* Build mechanisms to deal with dynamic changes: Plan for dynamic
set of resources, and not expect to work with a fixed set of
resources.
* Dynamic scaling requires automation in most cases, e.g., to turn
on new service instances. See again
[I-D.pignataro-enviro-sustainability-architecture] for a
discussion of automation.
* Interaction with the energy grid can enable dynamic load shifting.
For instance, a demand-response technique can be used where the
system temporarily reduces its energy usage in response to pricing
signals from a smart grid. The proposed demand-response technique
involves deferring the load from elastic requests to later time
periods in order to reduce the server demand and the current
energy usage, and hence, energy costs [LoadShifting].
* Energy-aware routing. This generally aims at aggregating traffic
flows over a subset of the network devices and links, allowing
other links and interconnection devices to be switched off. These
solutions shall preserve connectivity and QoS, for instance by
limiting the maximum utilization over any link, or ensuring a
minimum level of path diversity. There are also algorithms for
Green Traffic engineering. For instance [Segment] employs segment
routing. Experimental analysis results [Experiment] show that the
resource usage for SRv6 could be more than 70% lower than that of
the SPF-based forwarding, depending on the network topology.
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2.3.3. Recommendation
The guidelines above need to be considered specifically for each
protocol and system design. Further work in detailing this guidance
would also be useful.
It is likely that there is increased attention to resiliency in the
future, given for instance the increased importance of the tasks
supported by networks or the potentially increasing frequency of
natural disasters as a result of global warming.
2.4. Transport
Transport protocols are the flexible tools that make it possible for
communication flows between parties to adjust themselves to the
dynamic conditions that exist in the network at any given time:
available bandwidth, delays, congestion, the ability of a peer to
send or receive traffic, and so on. Depending on the conditions, an
individual flow may carry traffic at widely different rates, may
pause for some time, etc. Various higher-level transport solutions
may also cache or pre-fetch information.
This behavior has an effect on sustainability as well, e.g., in what
periods the endpoint and network systems are active or when they
could be in reduced activity or sleep states.
Cellular networks and mobile links can scale their energy usage based
on load and enter a low-power state when a traffic flow ends. Thus,
in theory, the faster the data is transferred, the faster the device
transmission/reception functions can enter a low-power state.
2.4.1. Motivation
Transport behavior would have a possibility of impacting how much
downtime or sleep can be had in the communication system, either on
the end systems or routers or other equipment in between. The
savings can be significant, at least in wireless systems.
Improvements through transport behavior are only useful if the
involved systems have power proportionality.
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2.4.2. Analysis
A critical issue is the tradeoff involved in sending traffic. As
argued in [NotTradeOff], reducing the amount of time the endpoints
and the network are active can sometimes help save energy, e.g. in
case the receiver is connected over a WiFi link. Similar logic
applies for any technology that has a certain degree of energy
proportionality, e.g. cellular communication. As a result, in
general, delivering information as rapidly as possible would appear
to be desirable.
On the other hand, bandwidth-intensive applications can influence
other applications or users by presenting a significant load on the
network, and consequently reducing capacity available for others, or
increasing buffering (and with it, latency) across the network path.
For an application with intermittent data transfers, such as
streaming video, this would seem to speak in favor of sustained but
lower-rate delivery instead of transmitting short high-rate bursts
[Sammy]. However, this is in contradiction with the energy-saving
approach above. Thus, the tradeoff is: should data be sent in a way
that is "friendly" to others (avoiding bad interference), or should
it save energy by sending fast, increasing the chance for equipment
to enter a "sleep" state?
At the time of writing, the common choice for video is to opt for
higher rate delivery, potentially saving energy, and possibly at the
expense of other traffic. For non-urgent data transfers, the IETF-
recommended default approach is the opposite: the LEDBAT congestion
control mechanism [RFC6817], which is designed for such use, will
always "step out of the way" of other traffic, giving it a low rate
when it competes with any other traffic. Alternatively, if the goal
is to reduce energy, such traffic could be sent at a high rate, at a
strategically good moment within a longer time interval; this would
give network equipment an opportunity to enter a sleep state in the
remaining time period within the interval.
Perhaps the issue is that the transport behavior (as with many other
things) needs to take into account multiple parameters. For example,
it is possible that a balanced transport algorithm would be able to
send as much as possible as soon as possible, while tracking buffer
growth from transmission delays and scaling back if there's any
buffer growth. This remains to be confirmed with experiments,
however.
Similarly, caching and pre-fetching designs need to take into account
not only the likelihood of having acquired the right content in
memory, but also the sustainability cost of possibly fetching too
much or the timing of those fetching operations.
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In general, information about the impacts of loading or not loading
the network with additional traffic, and whether a certain sending
pattern enables power savings through sleep modes, would be
beneficial for the communicating endpoints. Mechanisms for making
such information available to the endpoints would be useful.
2.4.3. Recommendation
The techniques described above have been based on theoretical
analysis. There is a need for further simulations and experiments to
confirm what strategies would provide the best end-user and energy
performance. This may be work that fits within the IRTF SUSTAIN
research group.
2.5. Equipment Longevity
This section discusses the ability to extend the useful life of
protocols and/or network equipment in order to amortize the embedded
energy costs over a longer period, even though it may mean that the
protocols/equipment may not be fully optimized for the present use.
This includes devising tools to inform network administrators and
their users of the potential benefits of network equipment upgrades,
so that they can make better choices on what upgrades are necessary
and when.
It should be noted that from an environmental sustainability
perspective, it may not always be the best choice to upgrade network
equipment whenever slightly less power-hungry and "greener"
alternatives become available. The environmental cost of amortizing
the carbon embedded inside equipment over its lifetime, including the
carbon associated with the manufacturing of the equipment that is to
be replaced, should be taken into consideration as well.
2.5.1. Motivation
Embedded carbon and raw materials can be a significant part of the
overall environmental impact of systems. If this can be improved for
devices that are manufactured in large quantities, the improvements
can be significant.
The more the world moves toward low-carbon energy sources, the more
the manufacturing matters in the holistic view. Today there can be
an order of magnitude difference in average emissions for a kWh of
electricity between two countries. Thus, any estimates that seek to
compare the manufacturing and use phase emissions of a network
equipment would have to be calculated per country or region, and
there is no universal standard for the whole planet.
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Long equipment lifetimes are only useful if the longer lifetimes can
be achieved without compromising other aspects of sustainability,
such as when using a high-end and power-hungry router in place of
small routers. The exact moment when a hardware change is warranted
for sustainability differs between countries and regions.
2.5.2. Analysis
When we engineer protocols and network equipment, we are inclined to
design them in a highly optimized manner for a very specific set of
requirements, use cases and context. While this is necessary in
certain cases (e.g. constrained nodes with limits on processing
capacity or long lived battery powered devices), there are certainly
cases where such optimized equipment is not absolutely required.
Most infrastucture network nodes on the Internet utilize only a
fraction of their design capacity most of the time.
Designing the equipment with an eye on longevity comes with a set of
advantages:
* It allows the same equipment and protocols be reused in a
different context in the future. e.g. A core router of today can
become an edge router in a near future and an access router in the
further future if the protocol implementations are adaptable.
* It can reduce complexity in implementations as well as in network
management that are usually indicated in highly optimized systems
* It can let network equipment operate for a longer period and can
reduce the frequency of hardware upgrades, in turn reducing the
environmental impact associated with manufacturing, transporting,
and disposing of the old/new hardware.
* One key disadvantage may be that not optimizing may result in the
need for premature upgrades for capacity and this needs to be
considered.
Hence, it is very likely that extending the life of protocols and
equipment with higher flexibility could provide a better
environmental benefit than tightly optimizing only for today’s uses.
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Another aspect that can play an important role in extending the
longevity of equipment concerns software-defined networking, in the
sense of designing networking equipment in such a way that new
equipment capabilities and features can be introduced via software
upgrades as opposed to requiring hardware replacement. This requires
system architectures that incorporate the necessary infrastructure to
support such upgrades in a secure manner that does not compromise
equipment integrity.
2.5.3. Recommendation
The guidelines above should be considered for any new system design.
If some aspect of protocol or network equipment design choice could
be made more generic and flexible without a significant performance
and sustainability impact, it needs to be studied in further detail.
Specifically, the potential additional sustainability costs due to
forgoing optimization need to be weighed against the potential
savings in embedded carbon and raw material costs brought about by
premature upgrades. There are also cases where equipment upgrades
are done to provide better peak performance characteristics (e.g.
higher advertised speeds towards consumers) and these need to be
viewed as well with the same tradeoffs in mind. Finally, when
designing networks it is recommended to consider whether it is
possible to reuse retiring equipment in a different location or for a
different function (e.g. move it to lower traffic geographies, core
routers become edge/access routers etc.)
2.6. Compact encoding
This is about considering the effects encoding methods on
sustainability, such as the use of binary encodings instead of text.
2.6.1. Motivation
Better encoding can obviously reduce the length of messages sent. It
remains a question mark how big overall impact this is, however. It
should only be performed if it gives a measurable overall impact.
2.6.2. Analysis
Better encoding methods are clearly beneficial for improving the
detailed-level effectiveness of communications.
The main questions are, however:
* Is the effect of this is at a magnitude comparable to the other
things, or if it is just absolutely tiny? Particularly
considering that much of the traffic on the Internet is video, and
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much of that is other content than, e.g., HTTP headers. Moran et
al. argued in their 2022 paper [CBORGreener] [RFC9547] that that
for a weather data example from [RFC8428] [RFC9193] there are
significant savings. However, this needs more research in terms
of the overall impact across different examples and the general
make up of Internet traffic.
* At what layer is the compactness achieved? Are link, IP, or
transport layer mechanisms that can compact some of the verbose
messaging useful, or should each protocol have optimal compacting?
* Tradeoffs related to compressing (particularly if AI-based
computationally expensive methods are used).
2.6.3. Recommendation
More research is needed to quantify the likely sources of measurable
impacts.
Of course, new protocols can generally be designed to work with
compact encoding, unless there is a significant reason not to. But
efforts to modify existing protocols for the sake of encoding
efficiency should be further investigated by the above mentioned
quantification results.
2.7. Sustainable by Design: Data Governance Perspective
Incorporating sustainability into the design phase of network
architecture is critical for ensuring long-term environmental and
operational benefits. From a Data Governance point of view,
"Sustainable by Design" involves embedding sustainability principles
and practices into the data management frameworks and processes from
the outset.
2.7.1. Motivation
Data governance plays a pivotal role in shaping how data is
collected, stored, processed, and used. By integrating
sustainability into these processes, organizations can ensure that
their data practices contribute to environmental goals, such as
reducing carbon footprints, optimizing resource usage, and minimizing
waste.
2.7.2. Analysis
Key elements of Sustainable by Design in data governance include:
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* Data Minimization: Collecting only the data that is necessary and
useful, reducing storage and processing requirements, which in
turn lowers energy consumption.
* Efficient Data Storage Solutions: Implementing energy-efficient
data storage technologies and practices that prioritize reduced
power usage and cooling needs.
* Lifecycle Management: Ensuring that data is managed throughout its
lifecycle in a way that minimizes environmental impact, including
secure and sustainable data disposal practices.
* Transparency and Accountability: Establishing clear data
governance policies that promote transparency in data usage and
accountability for sustainability objectives.
2.7.3. Recommendation
Organizations should adopt data governance frameworks that
incorporate sustainability as a core principle. This includes
setting clear sustainability goals, measuring progress towards these
goals, and continuously improving data management practices to
enhance sustainability. By doing so, organizations can ensure that
their data operations are not only effective but also environmentally
responsible.
3. Recommendations for Further Work and Research
Dynamic scaling, i.e., the ability to respond to demand variations
and resiliency requirements while optimizing energy consumption
clearly has significant potential for savings. Past and ongoing work
in various systems and protocols has looked at this, of course, but
we believe work also remains. Any large scale system likely benefits
from further analysis, unless already ongoing. Guidance in
{dynscale} simple, and further work in detailing this guidance would
also be useful.
Transport-related optimizations (see {transport}) that enable devices
to consume less power by sleeping more appear to have potential for
significant savings, but confirming this requires further research.
Such research could be performed in the context of the recently
chartered SUSTAIN research group.
More research is needed to quantify the likely sources of measurable
impacts when it comes to efficient protocol message encoding
discussed in {encoding}. Again, this is work that the research group
could take on.
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TBD
...
4. Security Considerations
It is possible that the introduction of features and architectural
properties to facilitate environmentally sustainable Internet
technology introduces new attack vectors or other security
ramifications.
For example, the introduction of measurements and metrics for the
purpose of saving energy could be misused for the opposite effect
when compromised. For example, measurements might be tampered with
in order to cause an operator to waste energy. Energy measurements,
when abused, might also result in compromised security, for example
by allowing to infer usage profiles. They could also be abused to
implement a covert communications channel in which information is
leaked via tampered measurement values that are being reported.
Networking features and technology choices may have security
implications regardless of why they are introduced, including for
reasons of environmental sustainability. The possibility of this
needs to be taken into consideration, understood, and communicated to
allow for their mitigation.
5. IANA Considerations
This document has no IANA actions.
6. Informative References
[Baseline] Livieratos, S., Panetsos, S., Fotopoulos, A., and M.
Karagiorgas, "A New Proposed Energy Baseline Model for a
Data Center as a Tool for Energy Efficiency Evaluation",
International Journal of Power and Energy Research, Vol.
3, No. 1 , April 2019.
[BenchmarkingFramework]
Mahadevan, P., Sharma, P., Banerjee, S., and P.
Ranganathan, "A Power Benchmarking Framework for Network
Devices", In L. Fratta et al. (Eds.): NETWORKING 2009,
LNCS 5550, pp. 795–808 , 2009.
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[CBORGreener]
Moran, B., Birkholz, H., and C. Bormann, "CBOR is Greener
than JSON", Position paper in the 2022 IAB Workshop
Environmental Impact of Internet Applications and
Systems , October 2022.
[Experiment]
Groningen, J. and C. Lung, "Green Network Traffic
Engineering Using Segment Routing: An Experiment Report",
2024 20th International Conference on Network and Service
Management (CNSM) , 2024.
[I-D.cparsk-eimpact-sustainability-considerations]
Pignataro, C., Rezaki, A., Krishnan, S., ElBakoury, H.,
and A. Clemm, "Sustainability Considerations for
Internetworking", Work in Progress, Internet-Draft, draft-
cparsk-eimpact-sustainability-considerations-07, 24
January 2024, <https://datatracker.ietf.org/doc/html/
draft-cparsk-eimpact-sustainability-considerations-07>.
[I-D.ietf-tvr-alto-exposure]
Contreras, L. M., "Using ALTO for exposing Time-Variant
Routing information", Work in Progress, Internet-Draft,
draft-ietf-tvr-alto-exposure-00, 23 December 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-tvr-
alto-exposure-00>.
[I-D.ietf-tvr-requirements]
King, D., Contreras, L. M., Sipos, B., and L. Zhang, "TVR
(Time-Variant Routing) Requirements", Work in Progress,
Internet-Draft, draft-ietf-tvr-requirements-05, 3 March
2025, <https://datatracker.ietf.org/doc/html/draft-ietf-
tvr-requirements-05>.
[I-D.ietf-tvr-schedule-yang]
Qu, Y., Lindem, A., Kinzie, E., Fedyk, D., and M.
Blanchet, "YANG Data Model for Scheduled Attributes", Work
in Progress, Internet-Draft, draft-ietf-tvr-schedule-yang-
03, 20 October 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-tvr-
schedule-yang-03>.
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[I-D.pignataro-enviro-sustainability-architecture]
Pignataro, C., Rezaki, A., Krishnan, S., Arkko, J., Clemm,
A., and H. ElBakoury, "Architectural Considerations for
Environmental Sustainability", Work in Progress, Internet-
Draft, draft-pignataro-enviro-sustainability-architecture-
01, 27 December 2024,
<https://datatracker.ietf.org/doc/html/draft-pignataro-
enviro-sustainability-architecture-01>.
[LinkAggregation]
"IEEE Standard for Local and Metropolitan Area Networks--
Link Aggregation", IEEE STD 802.1AX-2020 (Revision of IEEE
STD 802.1AX-2014): 1–333. doi:10.1109/
IEEESTD.2020.9105034. ISBN 978-1-5044-6428-4 , May 2020.
[LoadShifting]
Mathew, V., Sitaraman, R. K., and P. Shenoy, "Reducing
energy costs in Internet-scale distributed systems using
load shifting", Sixth International Conference on
Communication Systems and Networks (COMSNETS), Bangalore,
India, pp. 1-8, doi: 10.1109/COMSNETS.2014.6734894 , 2014.
[Modeling] Chen, C., Barrera, D., and A. Perrig, "Modeling Data-Plane
Power Consumption of Future Internet Architectures", IEEE
2nd International Conference on Collaboration and Internet
Computing (CIC), Pittsburgh, PA, USA, pp. 149-158, doi:
10.1109/CIC.2016.031 , 2016.
[NotTradeOff]
Welzl, M., "Not a Trade-Off: On the Wi-Fi Energy
Efficiency of Effective Internet Congestion Control", 17th
Wireless On-Demand Network Systems and Services Conference
(WONS), Oppdal, Norway, pp. 1-4, doi: 10.23919/
WONS54113.2022.9764413 , 2022.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012,
<https://www.rfc-editor.org/rfc/rfc6817>.
[RFC8428] Jennings, C., Shelby, Z., Arkko, J., Keranen, A., and C.
Bormann, "Sensor Measurement Lists (SenML)", RFC 8428,
DOI 10.17487/RFC8428, August 2018,
<https://www.rfc-editor.org/rfc/rfc8428>.
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[RFC9193] Keränen, A. and C. Bormann, "Sensor Measurement Lists
(SenML) Fields for Indicating Data Value Content-Format",
RFC 9193, DOI 10.17487/RFC9193, June 2022,
<https://www.rfc-editor.org/rfc/rfc9193>.
[RFC9547] Arkko, J., Perkins, C. S., and S. Krishnan, "Report from
the IAB Workshop on Environmental Impact of Internet
Applications and Systems, 2022", RFC 9547,
DOI 10.17487/RFC9547, February 2024,
<https://www.rfc-editor.org/rfc/rfc9547>.
[RFC9657] Birrane, III, E., Kuhn, N., Qu, Y., Taylor, R., and L.
Zhang, "Time-Variant Routing (TVR) Use Cases", RFC 9657,
DOI 10.17487/RFC9657, October 2024,
<https://www.rfc-editor.org/rfc/rfc9657>.
[Sammy] Bruce Spang, Shravya Kunamalla, Renata Teixeira, Te-Yuan
Huang, Grenville Armitage, Ramesh Johari, and Nick
McKeown, "Sammy: smoothing video traffic to be a friendly
internet neighbor", In Proceedings of the ACM SIGCOMM 2023
Conference (ACM SIGCOMM '23). Association for Computing
Machinery, New York, NY, USA, 754–768.
https://doi.org/10.1145/3603269.3604839 , 2023.
[Segment] Lung, C. and H. ElBakoury, "Exploiting Segment Routing and
SDN Features for Green Traffic Engineering", IEEE 8th
International Conference on Network Softwarization
(NetSoft), Milan, Italy, pp. 49-54, doi: 10.1109/
NetSoft54395.2022.9844091 , 2022.
[Unifying] Ishii, K., Kurumida, J., K.-i Sato, Kudoh, T., and S.
Namiki, "Unifying Top-Down and Bottom-Up Approaches to
Evaluate Network Energy Consumption", In Journal of
Lightwave Technology, vol. 33, no. 21, pp. 4395-4405, doi:
10.1109/JLT.2015.2469145 , November 2015.
[UNSDG] "United Nations Sustainable Development Goals",
https://unstats.un.org/sdgs , 2017.
Acknowledgments
Everyone on the author section has contributed to the document in
significant ways. The author list has been ordered in (reverse)
alphabethical order.
Parts of this document extensively leverage ideas and text from
[I-D.cparsk-eimpact-sustainability-considerations] and
[I-D.pignataro-enviro-sustainability-architecture] and associated
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discussions in the IETF, IRTF, and IAB groups. We acknowledge and
appreciate the many contributors whose work has enhanced its
development.
Authors' Addresses
Michael Welzl
University of Oslo
Email: michawe@ifi.uio.no
Emile Stephan
Orange
Email: emile.stephan@orange.com
Eve Schooler
University of Oxford
Email: eve.schooler@gmail.com
Sebastien Rumley
HES-SO
Email: sebastien.rumley@hes-so.ch
Ali Rezaki
Nokia
Email: ali.rezaki@nokia.com
Jukka Manner
Aalto University
Email: jukka.manner@aalto.fi
Carlos Pignataro
Blue Fern Consulting
Email: cpignata@gmail.com
Marisol Palmero
Cisco
Email: mpalmero@cisco.com
Jan Lindblad
All For Eco
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Email: jan.lindblad+ietf@for.eco
Suresh Krishnan
Cisco
Email: sureshk@cisco.com
Ari Keränen
Ericsson
Email: ari.keranen@ericsson.com
Hesham ElBakoury
Email: helbakoury@gmail.com
Luis M. Contreras
Telefonica
Email: contreras.ietf@gmail.com
Alexander Clemm
Independent
Email: ludwig@clemm.org
Jari Arkko
Ericsson
Email: jari.arkko@gmail.com
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