Internet DRAFT - draft-cx-green-metrics
draft-cx-green-metrics
Network Working Group A. Clemm
Internet-Draft L. Dong
Intended status: Informational Futurewei
Expires: 9 September 2023 G. Mirsky
Ericsson
L. Ciavaglia
Nokia
J. Tantsura
Microsoft
M-P. Odini
8 March 2023
Green Networking Metrics
draft-cx-green-metrics-02
Abstract
This document explains the need for network instrumentation that
allows to assess the power consumption, energy efficiency, and carbon
footprint associated with a network, its equipment, and the services
that are provided over it. It also suggests a set of related metrics
that, when provided visibility into, can help to optimize a network's
energy efficiency and "greenness".
Status of This Memo
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Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 4
3. Green Metrics . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Metrics related to Equipment . . . . . . . . . . . . . . 5
3.1.1. Energy Consumption Metrics . . . . . . . . . . . . . 5
3.1.2. Green Metrics Beyond Energy Consumption . . . . . . . 8
3.1.3. Virtualization Considerations . . . . . . . . . . . . 9
3.2. Green Metrics related to Flows . . . . . . . . . . . . . 10
3.3. Energy Metrics related to Paths . . . . . . . . . . . . . 11
3.4. Energy Metrics related to the Network-at-Large . . . . . 12
4. Other considerations . . . . . . . . . . . . . . . . . . . . 13
4.1. User perspective . . . . . . . . . . . . . . . . . . . . 13
4.2. Holistic perspective . . . . . . . . . . . . . . . . . . 13
4.3. Sustainable equipment production . . . . . . . . . . . . 14
4.4. Dealing with imprecision and uncertainty . . . . . . . . 15
4.5. Certification . . . . . . . . . . . . . . . . . . . . . . 15
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
6. Security Considerations . . . . . . . . . . . . . . . . . . . 16
7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 16
8. Informative References . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
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, reducing carbon
footprint is becoming of increasing importance for society and for
many industries. The networking industry is no exception.
Networks themselves consume significant amounts of energy and thus
contribute to greenhouse emissions. Therefore, the networking
industry has an important role to play in meeting sustainability
goals. Future networking advances will increasingly need to focus on
becoming more sustainable and reducing carbon footprint, both for
economic reasons and for reasons of corporate responsibility. Those
advances will focus first and foremost on greater energy efficiency,
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but not be limited to that. Other factors include consideration for
how power is being sourced (e.g. carbon versus solar based),
considerations for the lifecycle of hardware (e.g. software versus
forklift upgrades), and considerations related to deployments (for
example, minimizing the "energy tax" associated with heating or
cooling of networking devices). This shift has already begun and
sustainability is well on its way towards becoming an important
concern for network providers [telefonica2021].
There are many vectors along which networks can be made "greener".
At its foundation, it involves network equipment itself. Making such
equipment more energy-efficient is a big factor in helping networks
become greener. However, opportunities also exist at the level of
protocols themselves (e.g. reduction of transmission waste and
enabling of rapid control loops), at the level of the overall network
(e.g. path optimization under consideration of energy efficiency as a
cost factor), and architecture level (e.g. placement of contents and
functions) [I.D.draft-cwx-green-ps].
However, regardless of any particular approach that is chosen, in
order to assess its impact, there is a need to have visibility into
the actual energy consumption that is occurring and to ideally be
able to attribute that consumption to its sources. As the adage
goes, you cannot manage what you cannot measure. By extension, you
cannot optimize what you have no visibility of. The ability to
instrument networks in a way that allows for the assessment of energy
consumption is hence an important enabler for potential energy
optimizations, allowing to assess the effectiveness of measures that
are being taken and enabling (for example) control loops that involve
energy as an input. Before instrumenting, it needs to be clear,
however, what the proper metrics are that network providers will be
interested in and that applications will seek to optimize.
This document defines a set of metrics that allow to assess the
"greenness" of networks and that form the basis for optimizing energy
efficiency, carbon footprint, and environmental sustainability of
networks and the services provided. These metrics are intended to
serve the foundation for possible later IETF standardization
activities, such as the definition of related YANG modules [RFC7950]
or energy-related control protocol extensions. It should be noted
that the metrics introduced here are not intended to be used to
manage applications such as Power over Ethernet, requirements and
instrumentation for which have been defined in other contexts (e.g.
[RFC6988][RFC7460]).
Please note that throughout this document, we will be using the terms
"green", "sustainable", and "carbon footprint reduction"
interchangeably. Ultimately, the goal is to reduce (and as far as
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possible avoid) the emission of greenhouse gases. Emission of
greenhouse gases is generally caused by methods to generate energy
that is used to power devices (as well as production process for the
manufacturing of networking equipment or to heat, cool, light
buildings that house networking equipment). Within this context, we
will focus for the most part on energy efficiency and use that term
synonymously with "energy utilization efficiency", broadly speaking
referring to the efficiency with which energy is being utilized.
Energy efficiency contributes to reducing greenhouse gas emission by
minimizing the amount of required energy, not all of which might be
sourced sustainably. Other contributing factors include for example
the greenness of the energy source (such as solar versus fossil-
based).
2. Definitions and Acronyms
Carbon footprint: as used in this document, the amount of carbon
emissions associated with the use or deployment of technology,
usually directly correlated with the associated energy consumption
CPU: Central Processing Unit
IPFIX: IP Flow Information eXport
MTU: Maximum Transmission Unit
PSF: Power Sustainability Factor (PSF), a factor used to weigh
power consumption against the cleanliness of the underlying power
source
SDN: Software-Defined Networking
ST: Sustainability Tax, a factor applied to "raw" power
consumption metrics in order to account for factors such as the
sustainability of power sources in order to arrive a number that
reflects more closely the "true" contribution to carbon footprint.
TCAM: Ternary Content-Addressable Memory
VM: Virtual Machine
VNF: Virtual Network Function
Wh: Watt hour
pWh: pico Watt hour
kWh: kilo Watt hour
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3. Green Metrics
In the following, we categorize green metrics as follows:
* At the device/equipment level. This concerns aspects such as
energy consumption of a device as a whole, of equipment components
such as line cards or individual ports. It includes metrics that
would, for example, be found in equipment data sheets.
* At the flow level. This concerns aspects of carbon footprint that
can be attributed to flows. For example, this includes metrics
that attribute a device's share of its carbon footprint to the
flow, or metrics that aggregate energy consumption of packets
across the flow.
* At the path level. These metrics attest to the end-to-end carbon
footprint of paths, reflecting for example the amount of energy
drawn when the path is selected and taking into account the energy
efficiency and sustainability ratings of path segments.
* At the network level. These metrics aggregate sustainability
metrics across a network to provide a holistic picture of the
"network as a system". For example, this includes energy consumed
by the network as a whole and account also for aspects such as the
overall energy mix.
The green metrics that are defined are mostly comprised of energy
metrics, as required to assess and optimize various aspects of energy
efficiency. However, those energy metrics are complemented by
certain other metrics, for example metrics that account for the
sustainability of the energy source (where known), in turn allowing
to (for example) define derived metrics that discount metrics that
are based on measuring energy consumption by greenness factors.
3.1. Metrics related to Equipment
3.1.1. Energy Consumption Metrics
Arguably the most relevant green metrics relate to equipment. After
all, power is drawn from devices.
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The power consumption of the device can be divided into the
consumption of the core components (e.g., the backplane and CPU) as
well as additional consumption incurred per port and line card. In
[I.D.draft-manral-bmwg-power-usage], the device factors affecting
power consumption are summarized: base chassis power, number of line
cards, number of active ports, port settings, port utilization,
implementation of packet classification of Ternary Content-
Addressable Memory (TCAM) and the size of TCAM, firmware version.
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. Generally, the
cost of the first bit could be considered 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 CPU and memory that will be needed
to process additional packets becomes fairly negligible.
The first set of metrics corresponds to energy ratings of the device:
* Power consumption when idle (e.g. Watts)
* Power consumption when fully loaded (e.g. Watts)
* Power consumption at various loads: e.g. power consumption at 50%
utilization, power consumption at 90% utilization
These metrics should be maintained for the device as a whole, and for
the subcomponents, i.e.:
* For the chassis by itself
* For each line card
* For each port
They should also take into account aspects such as the current memory
configuration, as the overall energy consumption of a device is a
function of the energy consumption of the components that make up the
system.
The metrics could be provided by the data sheet associated with the
device or they could be measured as part of a deployment. For
maximum accuracy and comparability, they should reflect pre-defined
environmental setting, e.g., operating temperature, relative
humidity, barometric pressure. For example, ATIS (Alliance for
Telecommunications Industry Solutions) [ATIS0600015.02] defines a
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reference environment under which to measure router power
consumption: temperature of 25 celsius degree (within 3 celsius
degree deviation), relative humidity of 30% to 75%, barometric
pressure between 1020 and 812 mbar. In the AC power configuration,
the router should be evaluated at 230 VAC or within 1% deviation, 50
or 60 Hz or within 1% deviation [Ahn2014].
Please note that such metrics should ideally be certified. (See also
Section Section 4.5.)
The second set of metrics reflects the actual power being drawn
during operation. It is the type of data that might be provided as
management data. Again, it should be provided for the device as a
whole, as well as for the subcomponents reflected in the device
hierarchy: line cards, ports, etc.
* Current power consumption (e.g. Watts)
* Power drawn since system start (or module insertion, if at the
level of a line card, or port activation, if at the level of a
port), for the past minute (e.g. Watt hours)
The third set of metrics are derived from the earlier metrics. They
normalize the power consumption relative to the line speeds
respectively amount of traffic that is being passed.
* Current power consumption / kB (or gB)
* Current power consumption / packet
The fourth set of metrics reflects expectation values about
incremental energy usage. It could be relevant for use cases that
assess the cost of additional traffic. [Bolla2011] and [Ahn2014]
found that the power consumption of a router is in direct proportion
of the link utilization as well as the packet sizes. [Petrescu2010]
suggests using MTU-sized packets as a reference for energy usage.
(This is contrary to the earlier metric of current power consumption
per packet, which references the count of actual packets being
passed.)
* Incremental power per MTU-sized packet (possible units might be pJ
- pico Joules)
* Incremental power per gB
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3.1.2. Green Metrics Beyond Energy Consumption
In addition to consumption metrics, it is conceivable to also have
the device reflect other context of relevance. An important aspect
concerns the device's power source. In most cases, devices will be
agnostic to the power source and depend on the specific deployment.
Nonetheless, for a holistic picture, it makes sense to have the
"greenness" of the device power source reflected. This can occur,
for example, via a sustainability rating of the power source. This
sustainability rating might reflect sustainability on a scale ranging
from diesel-generator powered, powered via conventional power grid,
to powered via renewable energy (powered by windmill, capture of
excess heat, etc.). It may be possible to obtain such a rating from
the energy operator and (if not attributable to a single source)
reflect the operator's mix of energy sources. In some cases the
sustainability rating might vary with time: over long periods, as a
network operator's energy mix becomes more sustainable, as well as
over short periods, for example in the case of solar-powered devices
backed up by energy drawn from the grid.
Also, the environmental context of the device could be taken into
consideration, such as whether it is deployed in a data center and
its share in contributing to the need for cooling. It is conceivable
to, for example, introduce corresponding metrics that attribute a
share of the general power consumption of the network as a whole to
the device, including of the environment that the device is deployed
in (such as power drawn by the building that houses the device) - an
"energy tax" to be attributed to the device, so to speak. In
combination with a factor associated with a device's power
sustainability rating, this can result in an overall "pollution
factor" that allows to better assess the true contribution that a
device is making on carbon footprint. Weighing energy use by a
pollution factor, resulting in pollution-aware networking, has been
proposed in the literature as a more appropriate approach to
sustainable networks than mere energy-aware networking [Hossain2019].
Accordingly, as metrics, the following are being proposed:
* Power Sustainability Rating
* Power Sustainability Factor (PSF)
* Deployment Sustainability Factor
* Sustainability Tax (ST)
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It is conceivable to use PSF and ST to weigh other energy consumption
metrics in order to better express actual carbon contribution.
Corresponding metrics are easy to derive by applying PSF and/or ST as
a multiplication factor to the energy consumption metric. Doing so
will result in metrics such as the following:
* Current Sustainability-Weighed Power Consumption
* Current Sustainability-Weighed Power Consumption / gB
* Incremental Sustainability-Weighed Power Consumption / MTU-sized
packet
* etc.
As an option, it is conceivable to convert these metrics into
approximate CO2 emission metrics using some formula to calculate the
CO2-equivalent required to generate sustainability-weighed power.
(It should be noted that CO2 is of course not the only greenhouse
gas, but the one that is broadly recognized.)
3.1.3. Virtualization Considerations
Instrumentation should also take into account the possibility of
virtualization. This is important in particular as networking
functions may increasingly be virtualized and hosted (for example) in
a data center. Overlay networks may be formed. Likewise, many
applications expected to optimize energy consumption may be hosted on
controllers and applied to soft switches, VNFs (Virtual Network
Functions), or networking slices. The attribution of actual power
consumed to such virtualized entities is a non-trivial task. It
involves navigating layers of indirection to assess actual energy
usage and contribution by individual entities. While it would be
possible in such cases to simply revert to energy metrics of CPUs and
data centers as a whole, this loses the ability to account for those
metrics on the basis of networking decisions being made.
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 a router or a
switch, leading to very different power consumption characteristics.
A data center CPU could be more power efficient and consume power
more proportionally to actual CPU load. Virtualization could result
in using fewer servers. [Energystar] reports that one watt-hour of
energy savings at the server level results in roughly 1.9 watt-hours
of facility-level energy savings by reducing energy waste in the
power infrastructure and reducing energy needed to cool the waste
heat produced by the server. Of course, there are other tradeoffs to
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consider. For example, hosting certain functions at the edge instead
of the core may result in nominally higher carbon footprint when
viewed purely from the hosting infrastructure perspective. However,
it may decrease a network's carbon footprint overall due to a
reduction in long-distance traffic.
Instrumentation needs to reflect the reality that virtualization can
occur and facilitate attributing power consumption in a correct
manner. Ideally, the previously defined green metrics should be
transposed into equivalent virtual energy metrics. The
instrumentation of virtual energy metrics involves the attribution of
energy consumption and carbon footprint of real-world hosting
infrastructure to individual virtual functions that run on top of
that infrastructure. Doing so accurately may involve challenges.
However, equivalent capabilities have been defined before in the
context of cloud services running in data centers. In that context,
metrics have been proposed that attribute power usage to Virtual
Machines (VM) and allow to distinguish furthermore between idle VMs
(to determine waste), and all VMs (allowing to determine the ratio of
overall power consumed that is truly wasted) [vmWare2022]. As an
alternative, a simpler solution may be to simply forgo energy metrics
for virtualized functions entirely, instead focus on instrumenting
and relying on optimizing the energy footprint of the underlying
hosting infrastructure.
3.2. Green Metrics related to Flows
Green metrics related to flows attempt to capture the contribution of
a given flow to carbon footprint. In its basic incarnation, those
metrics reflect the energy consumption at a given device. They could
be used in conjunction with IPFIX [RFC7011] and modeled as
Information Elements to be treated analogous to other flow statistics
[RFC7012]. The following is a corresponding set of flow energy
metrics at a device:
* Incremental energy consumed over the duration of the flow.
This is the incremental energy consumption that is directly caused
by the flow, representing the difference between the amount of
energy consumed with the flow and the amount of energy that would
have been consumed without the flow. (It should be noted that
this metric may be difficult to assess in practice.)
* Amortized energy consumed over the duration of the flow.
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This is the portion of the flow's energy consumption for the
duration of the flow, effectively computed by computing the
proportion of flow traffic to overall traffic and multiplying it
with the total energy consumption incurred by the device for that
time.
A second set of metrics related to flow might aggregate the flow's
impact on carbon footprint over the entire flow path. In that case,
flow metrics observed at individual systems are added up along the
systems of the traversed path. However, in practice, this will be
much more difficult to assess with reasonable accuracy for many
reasons. These reasons include the impact of load balancing, PREOF
(Packet Replication, Elimination, and Ordering Functions [RFC8655])
which may lead to replicated packets for certain segments of a path
which still need to be attributed to the flow. The same is true for
packet loss, as lost packets may also contribute to the energy
equation. The carbon contribution of those packets until they were
dropped as well as their retransmission still needs to be attributed
to their respective flow. A third challenge concerns the ability to
trace actual routes taken by production traffic. On top of that,
there is the issue that other systems are involved at lower layers
whose contribution to carbon footprint may not be accounted for. For
these reasons, any metrics that are provided will need to come with
corresponding disclaimers as applicable.
Analogous to equipment metrics, metrics related to energy consumption
can further be weighed with PSF and ST to better reflect their actual
contribution to carbon footprint.
3.3. Energy Metrics related to Paths
Energy metrics related to paths involve assessing the carbon
footprints of paths and optimizing those paths so that overall
footprint is minimized, then applying techniques such as path-aware
networking [I.D.draft-chunduri-rtgwg-preferred-path-routing] or
segment routing [RFC8402] to steer traffic along those paths that are
deemed "the greenest" among alternatives. It also includes aspects
such as considering the incremental energy usage in routing
decisions, as has been suggested in proposals for energy-aware and
pollution-aware networking [Hossain2019].
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
includes the inclusion of energy-related parameters as a cost
parameter in control planes, whether distributed (e.g. IGP) or
conceptually centralized via SDN controllers.
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In addition to power consumption over a path itself, other factors
such as paths involving intermediate routers that are powered by
renewable energy resources might be considered, as might be
determined by an aggregate sustainability score. After all, paths
with devices that are powered by solar, wind, or geothermal might be
preferable over paths involving devices powered by conventional
energy that may include fossil fuel or nuclear resources.
The following are a corresponding set of candidate metrics:
* Energy rating of a path. (This could be computed as a function of
energy ratings of different hops along the path.)
* Current power consumption across a path. (This could be computed
by aggregating the current power per packet (or per kilo octet
etc) of each of the hops along the path.)
* Incremental power for a packet over a path. (This could be
computed by aggregating the incremental power per packet of each
of the hops along the path.)
Similar to some of the flow-related metrics, some caveats apply with
regards to challenges in capturing all contributors to carbon
footprint along a path. Specifically, it may be challenging to
account for the contribution of systems at lower layers to the
metrics of the path.
3.4. Energy Metrics related to the Network-at-Large
Ultimately, the goal of energy optimization and reduction of carbon
footprint is to minimize the aggregate amount of energy used across
the entire network, as well as to minimize the overall carbon
footprint of the network as a whole. Accordingly, metrics that
aggregate the energy usage across the network as a whole are needed.
In order to account for changing traffic profiles, growth in user
traffic etc, additional metrics are needed that normalize the total
over the volume of services supported and volume of traffic passed.
Corresponding metrics will generally be computed at the level of
Operational Support Systems (or Business Support Systems) for the
entire network.
Some of the metrics used include the following [telefonica2021]:
* Total energy consumption (MWh)
* Electricity from renewable sources (%)
* Network energy efficiency (MWh/PB)
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4. Other considerations
This document is intended to spark discussion about what metrics will
be useful to reduce the carbon footprint of networks - that provide
visibility into energy consumption, that help optimization of
networks under green criteria, that enable the next generation of
energy-aware controllers and services. Clearly, other metrics are
conceivable and more considerations apply beyond those that are
reflected in earlier sections of this document. The following
subsections highlight some of those items.
4.1. User perspective
Arguably, attributing energy usage to individual users and making
users aware of the sustainability implications of their communication
behavior may provide interesting possibilities to reduce
environmental footprint by guiding their behavior accordingly. For
example, the network could present clients with energy and carbon
statistics related to their communication usage. This could be
supported by metrics related to service instances, such as energy
usage statistics beyond statistics regarding volume, duration, number
of transactions. Such approaches would raise questions about how to
actually collect such statistics accurately (versus just computing
them via a formula) or what to actually include as part of those
statistics (amortized vs incremental energy contribution, attribution
of cost for path resilience or retransmissions due to congestion,
etc). They also raise questions about how they would in practice be
used. For example, energy-based charging might be explored as an
alternative for volume-based charging to incentivize carbon-conscious
networking use. However, in practice the two may be strongly
correlated and rejected by customers for similar reasons that volume-
based charging is frequently rejected.
4.2. Holistic perspective
The network itself is only one contributor to a network's carbon
footprint. Arguably just as important are aspects outside the
network itself, such as cooling and ventilation. These aspects need
to be taken into account as part of a holistic perspective. However,
reflecting such aspects in detail would arguably result in "boiling
the ocean" and are therefore not further addressed here.
That being said, clearly the carbon footprint and energy consumption
of a network as a whole will include non-negligible contributions of
devices beyond actively-managed networking equipment such as routers
or switches. As a result, the sum of metrics contributed across all
networking equipment may not reflect the total of the network as a
whole. In order to account for the contribution respectively carbon
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overhead of those hidden devices, one straightforward way is to
introduce a metric that provides the ratio of the sum of the known
contributions of devices versus the contribution of the network as a
whole. Such a metric can subsequently be factored in as an
additional "carbon tax" for other metrics where desired and
appropriate.
4.3. Sustainable equipment production
Internet energy consumption and associated carbon footprint may
comprise two major components [Raghavan2011]: (1) the energy of the
devices that construct the Internet, including the infrastructure
devices: routers, LAN devices, cellular and telecommunication
infrastructure, (2) More broadly, with the rise of peer-to-peer
applications and cloud services, it also considers the energy
consumption of the end systems, including desktops, laptops, smart
phones, cloud servers, and application servers that are not in the
cloud.
For those two components, the following factors need to take into
consideration for energy consumption calculation:
* Energy consumed in manufacturing of the devices and end-systems,
as well as the contribution from their components and materials.
This constitutes the embodied carbon footprint of the device. It
is conceivable to amortize embodied carbon footprint over the
lifetime of the device.
* The replacement lifespan of the devices and end-systems: desktops
and laptops are typically replaced in 3-4 years, smartphones in 2
years, application servers and cloud servers in 3 years, routers
and WiFi-LAN switches in 3 years, cellular towers and
telecommunication switches in 10 years, fiber optics in 10 years,
copper in 30 years, etc. With the incremental growth rate of the
technology advancement, the replacement lifespan might decrease
over time.
* Operational maintenance: the network would not be functional
without various software and implementation of protocols. The
energy consumed in creating software is complicated because it is
overwhelmingly human involved, which usually include the energy
used for the facilities of the software companies and human energy
of the programmers.
* Replacement: The energy consumed in replacement of devices and
end-systems could vary. Some could be very energy intensive for
those large devices, e.g., cellular towers, or environmental
unfriendly equipment, such as submarine communication cables.
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* Disposal: There is substantial energy cost in disposing and
recycling the old devices and equipment.
By combining the energy consumption for running each device that
builds the Internet [JuniperRouterPower], and the energy consumption
of the end systems, in the meantime counting the energy consumption
of manufacturing, operational maintenance, replacement and lifespan,
disposal of those devices and equipment, we may have an estimate of
the energy consumption for the network as a whole.
4.4. Dealing with imprecision and uncertainty
In some cases, it may be difficult to determine the values of metrics
precisely. This may be due to, for example, limitations of
instrumentation and/or the fact that consumption of energy (for
example) is neither constant nor linear but adheres to more complex
functions. In those cases, it may be advisable to allow for a way to
express metrics in ways that allow to reflect a degree of
uncertainty. For example, power consumption can be addressed not as
a single value but as a range defined by an upper and lower bound, as
suggested e.g. in [Petrescu2010] for the expression of power
consumption of links.
4.5. Certification
Some of the metrics that are mentioned in this document may be
difficult to assess and verify in practice, such as sustainability
ratings or device power ratings. As far as these metrics are used to
optimize the sustainability of network deployments, special
consideration needs to be given to ensure that those metrics are
indeed reflected correctly and accurately. Decisions that are based
on incorrect assumptions and data may lead to ineffective or even
counterproductive courses of actions. Where assessment and
specifically verification of certain metrics are difficult, solution
approaches that involve certification of those metrics (for example,
of sustainability ratings) by a trusted authority could be
considered.
5. IANA Considerations
This document does not have any IANA requests.
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6. Security Considerations
When instrumenting a network for energy metrics, it is important that
implementations are secured to ensure that data is accurately
measured and cannot be tampered with. For example, an attacker might
try to tamper energy readings to confuse controller trying to minize
power consumption, leading to increased power consumption instead.
In addition, access to the data needs to be secured in similar ways
as for other sensitive management data, for example using secure
management protocols and subjecting energy data that is maintained in
YANG datastores via NACM (NETCONF Access Control Model).
However, it should be noted that this draft specifies only metrics
themselves, not how to instrument networks accordingly. For the
definition of metrics themselves, security considerations do thus not
really apply.
7. Acknowledgments
We would like to thank the following persons for reviews and super-
helpful feedback on earlier versions of the document: Eve Schooler,
Michael Welzl, Alexandru Petrescu.
8. Informative References
[Ahn2014] Ahn, J. and H. S. Park, "Measurement and modeling the
power consumption of router interface",
DOI: 10.1109/ICACT.2014.6779082, 16th International
Conference on Advanced Communication Technology, pp.
860-863, 2014,
<https://ieeexplore.ieee.org/document/6779082>.
[ATIS0600015.02]
AITS, "Energy Efficiency for Telecommunication Equipment:
Methodology for Measurement and Reporting - Transport and
Optical Access Requirements", March 2016.
[Bolla2011]
Bolla, R., Bruschi, R., Lombardo, C., and D. Suino,
"Evaluating the energy-awareness of future Internet
devices", DOI: 10.1109/HPSR.2011.5986001, 2011 IEEE 12th
International Conference on High Performance Switching and
Routing, pp. 36-43, 2011,
<https://ieeexplore.ieee.org/document/5986001>.
[Energystar]
EnergyStar, "12 Ways to Save Energy in the Data Center,
Server Virtualization", 2022,
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<https://www.energystar.gov/products/
low_carbon_it_campaign/12_ways_save_energy_data_center/
server_virtualization>.
[Hossain2019]
Hossain, M., Georges, J., Rondeau, E., and T. Divoux,
"Energy, Carbon and Renewable Energy: Candidate Metrics
for Green-Aware Routing?", DOI: 10.3390/s19132901,
Sensors Vol. 19 No. 3, June 2019,
<https://ieeexplore.ieee.org/document/6779082>.
[I.D.draft-chunduri-rtgwg-preferred-path-routing]
Bryant, S. E., Chunduri, U., and A. Clemm, "Preferred Path
Routing Framework", May 2022,
<https://datatracker.ietf.org/doc/html/draft-chunduri-
rtgwg-preferred-path-routing-01>.
[I.D.draft-cwx-green-ps]
Clemm, A. and C. Westphal, "Challenges and Opportunities
in Green Networking", June 2022.
[I.D.draft-manral-bmwg-power-usage]
Manral, V., "Benchmarking Power usage of networking
devices", January 2011.
[JuniperRouterPower]
Juniper, "Power Requirements for an MX960 Router", 2021.
[Petrescu2010]
Petrescu, A., Janneteau, C., Olivereau, A., and M. Kellil,
"Energy Metric for IPv6 Links",
DOI: 10.13140/RG.2.1.4665.5209, March 2010,
<http://dx.doi.org/10.13140/RG.2.1.4665.5209>.
[Raghavan2011]
Raghavan, B. and J. Ma, "The energy and emergy of the
Internet", HotNets-X: Proceedings of the 10th ACM Workshop
on Hot Topics in Networks, pp. 1-6, 2011,
<https://dl.acm.org/doi/10.1145/2070562.2070571>.
[RFC6988] Quittek, J., Chandramouli, M., Winter, R., Dietz, T., and
B. Claise, "Requirements for Energy Management", RFC 6988,
September 2013,
<https://datatracker.ietf.org/doc/html/rfc6988>.
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[RFC7011] (Ed.), B. C., (Ed.), B. T., and P. Aitken, "Specification
of the IP Flow Information Export (IPFIX) Protocol for the
Exchange of Flow Information", RFC 7011, September 2013,
<https://datatracker.ietf.org/doc/html/rfc7011>.
[RFC7012] (Ed.), B. C. and B. T. (Ed.), "Information Model for IP
Flow Information Export (IPFIX)", RFC 7012, September
2013, <https://datatracker.ietf.org/doc/html/rfc7012>.
[RFC7460] Chandramouli, M., Claise, B., Schoening, B., Quittek, J.,
and T. Dietz, "Monitoring and Control MIB for Power and
Energy", RFC 7460, March 2015,
<https://datatracker.ietf.org/doc/html/rfc7460>.
[RFC7950] Bjorklund, M. E., "The YANG 1.1 Data Modeling Language",
RFC 7950, August 2016,
<https://datatracker.ietf.org/doc/html/rfc7950>.
[RFC8402] (Ed.), C. F., (Ed.), S. P., Ginsberg, L., Decraene, B.,
Decraene, B., Litkowski, S., and R. Shakir, "Segment
Routing Architecture", RFC 8402, July 2018,
<https://datatracker.ietf.org/doc/html/rfc8402>.
[RFC8655] Finn, N., Thubert, P., Varga, B., and J. Farkas,
"Deterministic Networking Architecture", RFC 8655, October
2019, <https://datatracker.ietf.org/doc/html/rfc8655>.
[telefonica2021]
Telefonica, "Telefonica Consolidated Annual Report 2021.",
2021.
[vmWare2022]
vmWare, "Definition for Metrics, Properties, and Alerts -
vRealize Operations 8.6 (pp.308ff)", May 2022,
<https://docs.vmware.com/en/vRealize-Operations/8.6/
vrealize-operations-86-reference-guide.pdf>.
Authors' Addresses
Alexander Clemm
Futurewei
2220 Central Expressway
Santa Clara, CA 95050
United States of America
Email: ludwig@clemm.org
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Lijun Dong
Futurewei
2220 Central Expressway
Santa Clara, CA 95050
United States of America
Email: lijun.dong@futurewei.com
Greg Mirsky
Ericsson
Email: gregimirsky@gmail.com
Laurent Ciavaglia
Nokia
Email: laurent.ciavaglia@nokia.com
Jeff Tantsura
Microsoft
Email: jefftant.ietf@gmail.com
Marie-Paule Odini
Email: mp.odini@orange.fr
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