Green Networking Metrics
draft-cx-opsawg-green-metrics-02
| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Alexander Clemm , Lijun Dong , Greg Mirsky , Laurent Ciavaglia , Jeff Tantsura , Marie-Paule Odini , Eve Schooler , Ali Rezaki , Carlos Pignataro | ||
| Last updated | 2024-03-04 | ||
| Replaces | draft-cx-green-metrics | ||
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draft-cx-opsawg-green-metrics-02
Network Working Group A. Clemm, Ed.
Internet-Draft L. Dong
Intended status: Informational Futurewei
Expires: 5 September 2024 G. Mirsky
Ericsson
L. Ciavaglia
Nokia
J. Tantsura
Nvidia
M-P. Odini
E. Schooler
A. Rezaki
Nokia
C. Pignataro, Ed.
NC State University
4 March 2024
Green Networking Metrics
draft-cx-opsawg-green-metrics-02
Abstract
This document explains the need for network instrumentation that
allows to assess a number of sustainability-related attributes such
as 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
"greenness" accordingly.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
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This Internet-Draft will expire on 5 September 2024.
Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
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provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 4
3. Green Metrics . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Metrics related to Equipment . . . . . . . . . . . . . . 7
3.1.1. Energy Consumption Metrics . . . . . . . . . . . . . 7
3.1.2. Green Metrics Beyond Energy Consumption . . . . . . . 10
3.1.3. Virtualization Considerations . . . . . . . . . . . . 12
3.2. Green Metrics related to Flows . . . . . . . . . . . . . 13
3.3. Energy Metrics related to Paths . . . . . . . . . . . . . 14
3.4. Energy Metrics related to the Network-at-Large . . . . . 15
4. Other considerations . . . . . . . . . . . . . . . . . . . . 16
4.1. User perspective . . . . . . . . . . . . . . . . . . . . 16
4.2. Holistic perspective . . . . . . . . . . . . . . . . . . 17
4.3. Sustainable equipment production . . . . . . . . . . . . 17
4.4. Dealing with imprecision and uncertainty . . . . . . . . 18
4.5. Certification . . . . . . . . . . . . . . . . . . . . . . 19
4.6. Green metrics defined elsewhere . . . . . . . . . . . . . 19
5. Controversies . . . . . . . . . . . . . . . . . . . . . . . . 19
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21
7. Security Considerations . . . . . . . . . . . . . . . . . . . 21
8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21
9. Informative References . . . . . . . . . . . . . . . . . . . 21
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 25
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1. Introduction
Climate change and the need to curb greenhouse emissions have been
recognized by the United Nations and by many governments as one of
the biggest and most urgent challenges of our time [UN-IPCC-2023].
As a result, reducing carbon footprint is becoming of increasing
importance for society and all 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 social responsibility.
Those advances initially focus on improving energy efficiency and
will continue in several other areas. Of equivalent important is how
power is being sourced (e.g., carbon versus solar based). Other
factors include considerations for the lifecycle of hardware (e.g.,
embedded carbon during material extraction and manufacturing,
transportation, software versus forklift upgrades, consumption of
cloud-delivered), and considerations related to deployments (for
example, minimizing the "sustainability 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]. A broader underlying
background and analysis on sustainability considerations for
networking can be found at
[I-D.cparsk-eimpact-sustainability-considerations].
There are many vectors along which networks can be made "greener".
At its foundation, it involves network equipment itself. For
example, 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). A good overview of such
opportunities and associated challenges is provided in
[I-D.cx-green-ps].
Regardless of any particular approach that is chosen, in order to
assess its impact, there is a need to have visibility. For example,
techniques that attempt to minimize energy consumption may need
visibility into the actual energy consumption that is occurring in a
network and to ideally be able to attribute that consumption to what
is causing it. As the adage goes, you cannot manage what you cannot
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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 factors related to a network's
environmental footprint is hence an important enabler for potential
optimizations that help to reduce that footprint. Not only does it
allow to assess the effectiveness of measures being taken, but it
also enables (for example) control loops based on those factors.
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. The importance of such metrics
has also been highlighted by the IAB [RFC9547]
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]).
One key goal is to reduce (and as far as possible avoid) the emission
of greenhouse gases when operating networks while continuing to
provide communication services that meet user demands. 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, a
key focus is "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 that will be touched
upon include, for example, the carbon intensity of the energy source
(such as solar versus fossil-based), and the carbon that is embedded
within a device. It should be noted that due to those, as well as
other contributing factors, energy efficiency and carbon efficiency
related but not the same [Shenoy2022].
2. Definitions and Acronyms
A comprehensive set of definitions and acronym expansions can be
found at [I-D.cparsk-eimpact-sustainability-considerations], readers
are encouraged to refer to it.
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Carbon footprint, use-phase: the amount of carbon emissions
associated with the use of technology, usually directly correlated
with the associated energy consumption
CPU: Central Processing Unit
DSF: Deployment Sustainability Factor, a factor to weigh power
consumption in a way that also reflects the power consumption of
the overall deployment including non-network equipment such as
cooling.
IPFIX: IP Flow Information eXport
Green: Sustainable
MTU: Maximum Transmission Unit
Power Consumption: The total amount of electrical energy used over
a unit of time
Power Draw: The amount of power drawn, i.e. the amount of
electrical energy used at a given moment
PSF: Power Sustainability Factor, 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
Additionally, this document uses the following metrics-related terms.
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Conversion Metric: A type of derived metric that can be calculated
from a single other metric by applying a conversion formula
Derived Metric: A metric whose value depends on other metrics from
which it can be computed (or "derived")
Primary Metric: A metric that needs to be measured, i.e., that
cannot be computed from other metrics or factored
3. Green Metrics
In the following, we categorize green metrics according to the
subject of the 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.
* At the flow level. This concerns metrics that can be attributed
to flows. For example, this includes metrics that could attribute
a device's share of its carbon footprint to a given flow, or
metrics that aggregate energy consumption of packets across the
flow. A flow is defined as per the IPFIX [RFC7011] context.
* At the path level. These metrics attest to the end-to-end green
metrics of paths, reflecting for example the amount of energy
drawn when the path is selected, taking into account the energy
efficiency and sustainability ratings of path segments across the
path.
* At the network domain level. These metrics aggregate
sustainability metrics across a network domain to provide a
holistic picture of the "network domain as a system". For
example, this includes energy consumed by the network as a whole
and may account also for aspects such as the overall energy mix.
Topological considerations are important at the network domain
level.
The green metrics that are defined are mostly comprised of energy
metrics, as required to assess, and optimize various aspects of
energy consumption and 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).
We furthermore distinguish between primary metrics which are directly
measured, and derived metrics which are computed from multiple
factors. For example, a primary metric might be the power
consumption of a device, while a derived metric might be a metric
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that relates energy consumption to utility provided, for example
power consumption per gB of traffic passed. In general,
instrumentation will focus on primary metrics that need to be
measured where they occur, while derived metrics can be computed from
other metrics.
A special case of derived metrics are conversion metrics which are
based on conversion from other metrics by some factor. An example
would be metrics that convert energy use into carbon footprint, using
a formula to compute emitted CO2 based on energy use using some
factor. Another example would involve the use of sustainability
factors that reflect the energy mix used to power a given piece of
equipment, resulting in metrics reflecting discounted energy use
based on those 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.
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.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.
Depending on the type of device, there may also be other factors,
such as radios in case of equipment supporting wireless transmission.
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 negligible. Of course, precise
numbers vary greatly between different devices and device
architectures, some of which may support dynamic sleep state models
that are able to transition quickly with limited overhead, thus
mitigating some of those effects.
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In the following, sets of metrics are defined that are deemed useful
to assess sustainability of network technology at the device level.
These metrics are defined independently of their particular
representation as part of a data model, for example a YANG data
model. The definition of such data models is outside the scope of
this document. An example of such a YANG data model used can be
found in [I-D.opsawg-poweff], concerned with power efficiency of
networking devices. A second example can be found in
[I-D.li-ivy-power], concerned less with metrics but with control
knobs to help manage power saving modes of network devices in an
inventory. Either of these data models are expected to contain
representations for metrics that are defined here as applicable.
The first set of metrics corresponds to energy ratings of the device.
Such metrics can be useful for purposes such as planning of network
deployments or optimization of configuration of paths. They also
provide a good proxy to model expected actual energy use in a
network.
* Power draw when idle (e.g., Watts)
* Power draw when fully loaded (e.g., Watts)
* Power draw at various loads: e.g., at 50% utilization, 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 would not necessarily need to be instrumented as they
could be provided by the data sheet associated with the device or
they could be measured in a test lab or 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
reference environment under which to measure router power
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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].
It should be noted that just because a metric is stated does not
necessarily mean in all cases that it is accurate or true. Where
this can be a concern, they should ideally be certified. (See also
Section 4.5.)
The second set of metrics are primary metrics that reflect the actual
power being drawn during operation. It is the type of data that
might be provided as management data. Possible uses include
accounting for actual power usage and comparing actual with expected
consumption to refine and calibrate consumption models. Again,
metrics 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 draw (e.g., Watts)
* Power consumed 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 is a set of derived metrics that are derived
from the earlier metrics. They normalize the power consumption
relative to the line speeds respectively to the amount of traffic
that is being passed. In effect this allows to assess the share of
the total power consumption that would be attributed for each unit of
traffic. Rather than assessing absolute power consumption, they
relate power that is consumed to functionality provided in order to
provide measures of efficiency. for use by applications that aim to
optimize efficiency. These metrics might be computed by devices
themselves or computed after the fact by controllers or managing
systems that collect the underlying primary metrics.
* Current power consumption / kB (or gB)
* Current power consumption / packet
It should be noted that efficiency metrics are not without
controversy, as the amount of traffic may not be reflective of the
actual utility being derived by users of communication services.
Volume of traffic or number of packets by itself is in many cases not
indicative of such utility. Where feasible, it makes sense to
complement these basic efficiency metrics with more refined
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efficiency metrics that take the utility delivered to applications
better into account, such as power consumption per minute of video
delivered. Such metrics may be more meaningful to users of services,
at the same time they may be harder to assess since dependent also on
other application-specific factors (such as supported codecs), depend
on specifics of a particular application of which the network may not
be aware, and not reflect the actual mix of applications being used.
The fourth set of metrics reflects expectation values about
incremental energy usage. These metrics could be relevant for use
cases that assess the cost of additional traffic. [Bolla2011] and
[Ahn2014] found that incremental power consumption (between baseline
power usage at idle and full utilization) 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. (It should be noted that incremental energy use for
additional packets is different from the earlier metric of current
power consumption per packet, which equally allocates power
consumption among all packets being passed.)
* Incremental power consumption per MTU-sized packet (possible units
might be pJ - pico Joules)
* Incremental power consumption per gB
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. Even in cases where a power
source does not independently provide such data, it is conceivable to
use controllers and/or management systems to provision certain
devices with it to make those device and the network aware of it to
allow network-embedded algorithms to take such information into
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account.
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) - a
"sustainability 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 Factor (PSF). This factor reflects the
sustainability of the energy mix that is used to power the device.
When multiplied with actual power consumption, it can provide a
"weighted" power consumption that accounts for (for example) the
portion of energy that is renewable. This is typically referred
to as Carbon Intensity, or Electricity emission factor.
* Deployment Sustainability Factor (DSF). This factor reflects a
factor to attribute a share of a deployment's overall power
consumption (beyond that directly caused by networking devices) to
individual network devices.
It should be noted that usually these factors will fluctuate with
time. For example, a solar-powered device backed up by the
electrical grid may exhibit a different PSF depending on factors such
as time-of-day, battery status, and weather. In many cases they will
represent merely approximations. In general, they may also not be
measured but assigned by network providers, e.g., provisioned on a
device. As a result, they should be considered as a tool that can be
used to refine sustainability optimizations in a network, but not be
misconstrued as a measure of absolute truth regarding actual
greenness of a device.
It is conceivable to use PSF and DSF to weigh other energy
consumption metrics in order to better express actual carbon
contribution. (The above caveats regarding those factors apply, as
well as the caveat to caution against relying solely on weighted
metrics which heavily depend on choice of underlying factors which
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could be misused to lead to misleading results.) Corresponding
metrics are easy to derive by applying PSF and/or DSF as a
multiplication factor to the energy consumption metric. Doing so
will result in metrics such as the following:
* Current Sustainability-Weighed Power Draw
* 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-weighted power.
It is possible to define a corresponding set of conversion metrics.
(It should be noted that CO2 is of course not the only greenhouse
gas, but the one that is most 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:
* Amortized energy consumed over the duration of the flow.
This is the share of the flow's energy consumption of the total
energy consumed over the duration of the flow. This can be
effectively computed by determining the ratio of flow traffic as a
share of overall traffic and multiplying it with the total energy
consumption incurred by the device over that time. (As with other
metrics, the effort and power consumption needed to measure this
data needs to be taken account. For example in this case, rather
than attempting to perform highly-granular measurements at every
instant of time or every packet, approximations such as
attributing the energy consumption as a share of total traffic
over the duration of the flow will be entirely sufficient.)
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* 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.)
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 weighted 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.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].
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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.
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 or PSFs of different hops along the path. For
example, it could be the maximum PSF of any path segment (to avoid
use of any path segments deemed particularly "dirty"), or the sum
of PSFs across all path segments (to reflect the "true cost" of
the path in its entirety), or the average PSF of path segments.)
* Current power consumption across a path, also referred to as Path
Energy Traffic Ratio [I-D.petra-path-energy-api]. (This could be
computed by aggregating the current power per packet (or per kB
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
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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:
* Total energy consumption (MWh), the entire energy consumption that
can be attributed to the network [Telefonica2021]
* Electricity from renewable sources (%), the percentage of total
energy consumption that comes from renewables [Telefonica2021]
* Network energy efficiency (MWh/PB), relating total energy
consumption to the utility derived from the network as measured by
the total amount of data being transmitted [Telefonica2021]
* Energy efficiency rating (EER), the ratio between network net
energy consumption of networking devices and the total energy
consumption [ETSI2023-EEPS65]
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
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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
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 "sustainability tax" (or "carbon tax" - not in the
monetary but in a technical sense) 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:
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* 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.
* 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
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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.
4.6. Green metrics defined elsewhere
Other standardization organization have considered sustainability of
networking as well. Notably, this includes the ETSI Technical
Committee on Environmental Engineering (TC EE), which has producing
standards relating to the measurement of energy efficiency of various
network elements and network segments [ETSI203228][ETSI202706-1][ETSI
202706-2][ETSI303215][ETSI203184][ETSI203136]. Specifically, this
includes metrics regarding the power consumption and energy
efficiency of network equipment, particularly in mobile networks but
also more generally in fixed access and transport and IP networks.
Beyond energy consumption metrics for equipment, these standards also
specify certain other aspects such as performance and efficiency
metrics related to data volume, mobile network coverage, and latency,
as well as measurement and extrapolation methodologies. While some
of these aspects exceed the scope of the document here, we expect
these standards to provide a good reference point for the definition
of metrics related to the energy efficiency metrics. Future
revisions of this document will therefore consider which of those
metrics make sense to adopt here, pending further analysis.
5. Controversies
There are many ways in which the metrics defined in this document can
be used. One of those uses includes assessment of the "greenness" of
networks, as the metrics presented here allow (among other things) to
gauge progress over time as well as define benchmarks used for
comparison. Another important use includes the ability to use those
metrics for optimizing the network, enabling (for example) feedback
loops that observe the outcomes of configuration measures taken and
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conduct subsequent tweaks with the goal of improving those outcomes.
One problem with selecting any particular metric concerns that it can
be "gamed", painting a distorted picture in which, while one metric
may look great, the same may not be true for the overall
sustainability outcome. For example, only looking at total energy
consumption of a network as a whole misses the fact how much utility
was provided by the network overall. Deployments may grow over time,
traffic mix changes, all of which will impact the metric without
being self-evident. Similarly, looking only at efficiency metrics
such as power consumption / gB may not take into account of the
embedded carbon footprint of a forklift upgrade that may offset
smaller nominal efficiency gains. Similarly, gB by themselves are
not a comprehensive measure for utility, which would include also
other factors such as service levels delivered or actual goodput
achieved.
However, controversies surrounding the use of individual metrics in
isolation can be mitigated by providing a basket of metrics that
collectively provide a more nuanced picture. Similarly, the context
in which metrics are used plays an important role to not be ignored.
Is a particular metric used as a basis for promotional material to
greenwash a network provider's operations, possibly as the only
metric? Or is it used by the same provider as one of many metrics
used to assess progress achieved in their network over time?
The stance taken in this document is therefore:
* No individual metric defined in this document paints a
comprehensive picture by itself. Instead, metrics are defined to
complement one another, and generally speaking multiple metrics
should be used in combination to result in a more holistic picture
and lead to more representative outcomes.
* Care should be taken when using individual metrics for comparison
purposes. For example, different deployments may vary wildly in
terms of their purpose, services provided, and operational goals,
which may render the use of individual metrics for comparisons of
which is "better" meaningless. However, comparisons to track
progress over time may still make sense. Again, combinations of
metrics paint more nuanced pictures than metrics that are
isolated.
* Benchmarking may be a technique to result in greater
comparability. The metrics defined in this document can be used
by benchmarks; however, the development of benchmarks is beyond
the scope of this document.
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6. IANA Considerations
This document does not have any IANA requests.
7. Security Considerations
When instrumenting a network for energy metrics, it is important that
implementations are secured to ensure that data is accurately
measured, communicated, and cannot be tampered with. For example, an
attacker might try to tamper energy readings to confuse controller
trying to minimize 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). Specifically, these metrics need to have signed
origin, traceability, and optional cryptographic protection.
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 not
directly apply.
8. Acknowledgments
We would like to thank Michael Welzl, Alexandru Petrescu, and Jari
Arkko for reviews and super-helpful feedback on earlier versions of
the document.
9. 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]
ATIS, "Energy Efficiency for Telecommunication Equipment:
Methodology for Measurement and Reporting - Transport and
Optical Access Requirements", March 2016.
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[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,
<https://www.energystar.gov/products/
low_carbon_it_campaign/12_ways_save_energy_data_center/
server_virtualization>.
[ETSI2023-EEPS65]
ETSI, "DES/EE-EEPS65: Environmental Engineering (EE):
Fixed Network Energy Efficiency definition and measurement
(Work Item)", October 2023.
[ETSI202706-1]
ETSI, "ES 202 706-1: Metrics and measurement method for
energy efficiency of wireless access network equipment;
Part 1: Power consumption - static measurement method",
August 2022.
[ETSI202706-2]
ETSI, "TS 102 706-2: Metrics and measurement method for
energy efficiency of wireless access network equipment;
Part 2: Power consumption - dynamic measurement method",
November 2018.
[ETSI203136]
ETSI, "ES 203 136: Measurement methods for energy
efficiency of router and switch equipment", August 2017.
[ETSI203184]
ETSI, "ES 203 184: Measurement Methods for Power
Consumption in Transport Telecommunication Networks
Equipment", December 2012.
[ETSI203228]
ETSI, "ES 203 228: Assessment of mobile network energy
efficiency", October 2020.
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[ETSI303215]
ETSI, "EN 303 215: Measurement methods and limits for
power consumption in broadband telecommunication networks
equipment", December 2014.
[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.chunduri-rtgwg-preferred-path-routing]
Bryant, S., Chunduri, U., and A. Clemm, "Preferred Path
Routing Framework", Work in Progress, Internet-Draft,
draft-chunduri-rtgwg-preferred-path-routing-03, 7 November
2022, <https://datatracker.ietf.org/doc/html/draft-
chunduri-rtgwg-preferred-path-routing-03>.
[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.cx-green-ps]
Clemm, A., Westphal, C., Tantsura, J., Ciavaglia, L., and
M. Odini, "Challenges and Opportunities in Management for
Green Networking", Work in Progress, Internet-Draft,
draft-cx-green-ps-02, 13 March 2023,
<https://datatracker.ietf.org/doc/html/draft-cx-green-ps-
02>.
[I-D.li-ivy-power]
Li, T. and R. Bonica, "A YANG model for Power Management",
Work in Progress, Internet-Draft, draft-li-ivy-power-01,
17 October 2023, <https://datatracker.ietf.org/doc/html/
draft-li-ivy-power-01>.
[I-D.manral-bmwg-power-usage]
Manral, V., Sharma, P., Banerjee, S., and Y. Ping,
"Benchmarking Power usage of networking devices", Work in
Progress, Internet-Draft, draft-manral-bmwg-power-usage-
04, 12 March 2013, <https://datatracker.ietf.org/doc/html/
draft-manral-bmwg-power-usage-04>.
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[I-D.opsawg-poweff]
Lindblad, J., Mitrovic, S., Palmero, M., and G. Salgueiro,
"Power and Energy Efficiency", Work in Progress, Internet-
Draft, draft-opsawg-poweff-00, 20 October 2023,
<https://datatracker.ietf.org/doc/html/draft-opsawg-
poweff-00>.
[I-D.petra-path-energy-api]
Rodriguez-Natal, A., Contreras, L. M., Muniz, A., Palmero,
M., and F. Munoz, "Path Energy Traffic Ratio API (PETRA)",
Work in Progress, Internet-Draft, draft-petra-path-energy-
api-00, 14 September 2023,
<https://datatracker.ietf.org/doc/html/draft-petra-path-
energy-api-00>.
[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., Ed., Chandramouli, M., Winter, R., Dietz, T.,
and B. Claise, "Requirements for Energy Management",
RFC 6988, DOI 10.17487/RFC6988, September 2013,
<https://www.rfc-editor.org/info/rfc6988>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC7012] Claise, B., Ed. and B. Trammell, Ed., "Information Model
for IP Flow Information Export (IPFIX)", RFC 7012,
DOI 10.17487/RFC7012, September 2013,
<https://www.rfc-editor.org/info/rfc7012>.
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[RFC7460] Chandramouli, M., Claise, B., Schoening, B., Quittek, J.,
and T. Dietz, "Monitoring and Control MIB for Power and
Energy", RFC 7460, DOI 10.17487/RFC7460, March 2015,
<https://www.rfc-editor.org/info/rfc7460>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.
[RFC8402] Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L.,
Decraene, B., Litkowski, S., and R. Shakir, "Segment
Routing Architecture", RFC 8402, DOI 10.17487/RFC8402,
July 2018, <https://www.rfc-editor.org/info/rfc8402>.
[RFC8655] Finn, N., Thubert, P., Varga, B., and J. Farkas,
"Deterministic Networking Architecture", RFC 8655,
DOI 10.17487/RFC8655, October 2019,
<https://www.rfc-editor.org/info/rfc8655>.
[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/info/rfc9547>.
[Shenoy2022]
Shenoy, P., "Energy-Efficiency versus Carbon-Efficiency:
What's the difference?", DOI: 10.1145/3584024.3584025, ACM
SIGEnergy Energy Informatics Review, Vol 2 Issue 4,
December 2022,
<https://dl.acm.org/doi/abs/10.1145/3584024.3584025>.
[Telefonica2021]
Telefonica, "Telefonica Consolidated Annual Report 2021.",
2021.
[UN-IPCC-2023]
UN, "Intergovernmental Panel on Climate Change, IPCC AR6
Synthesis Report: Climate Change 2023", March 2023.
[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
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Alexander Clemm (editor)
Futurewei
2220 Central Expressway
Santa Clara, CA 95050
United States of America
Email: ludwig@clemm.org
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
Nvidia
Email: jefftant.ietf@gmail.com
Marie-Paule Odini
Email: mp.odini@orange.fr
Eve Schooler
Email: eve.schooler@gmail.com
Ali Rezaki
Nokia
Email: ali.rezaki@nokia.com
Carlos Pignataro (editor)
North Carolina State University
United States of America
Email: cpignata@gmail.com, cmpignat@ncsu.edu
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