Computing Energy Consumption Path in Segment Routing Networks
draft-liu-spring-sr-policy-energy-efficiency-05
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Yisong Liu , Changwang Lin , Ran Chen , Jinming Li , Luis M. Contreras | ||
| Last updated | 2026-03-02 | ||
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draft-liu-spring-sr-policy-energy-efficiency-05
SPRING Y. Liu
Internet-Draft China Mobile
Intended status: Standards Track C. Lin
Expires: 3 September 2026 New H3C Technologies
R. Chen
ZTE Corporation
J. Li
China Mobile
L. M. Contreras
Telefonica
2 March 2026
Computing Energy Consumption Path in Segment Routing Networks
draft-liu-spring-sr-policy-energy-efficiency-05
Abstract
This document elaborates on the method for calculating energy
consumption paths in Segment Routing (SR) networks, aiming to
evaluate and optimize traffic-related metrics on network paths,
including energy consumption and carbon emissions.
Status of This Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 3 September 2026.
Copyright Notice
Copyright (c) 2026 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.
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Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. Background . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Energy consumption parameters . . . . . . . . . . . . . . . . 5
3.1. Energy Efficiency: . . . . . . . . . . . . . . . . . . . 6
3.2. Renewable electricity usage ratio & carbon emission
factor: . . . . . . . . . . . . . . . . . . . . . . . . . 6
4. Private Line Carbon Accounting Mechanism . . . . . . . . . . 7
4.1. Energy Consumption Collection . . . . . . . . . . . . . . 7
4.2. Path Calculation Based on Energy Consumption . . . . . . 8
4.3. Issuance of Path . . . . . . . . . . . . . . . . . . . . 8
5. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1. Dynamic link shutdown . . . . . . . . . . . . . . . . . . 9
5.2. Network Path Carbon Emission Assessment . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 11
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Normative References . . . . . . . . . . . . . . . . . . . . . 11
Informative References . . . . . . . . . . . . . . . . . . . . 12
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
With the accelerated global digital transformation, the scale and
complexity of network infrastructure have grown exponentially. The
accompanying energy consumption and carbon emission issues have
become key bottlenecks restricting the sustainable development of the
industry. Building a green, low-carbon, and efficient network
operation system is no longer merely a means of cost control, but has
evolved into a crucial core research direction in the field of
information and communication technology. How to achieve energy
conservation and emission reduction through intelligent resource
scheduling and path optimization while ensuring network service
quality has become a common focus.
Existing research and practices mainly focus on two scenarios:
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First, the dynamic sleep mechanism based on the traffic tidal effect.
Given that network traffic exhibits significant periodic fluctuations
in temporal and spatial distribution (i.e., the "tidal phenomenon"),
this strategy can, during off-peak business periods (trough periods),
use the network controller to accurately identify idle links,
dynamically shut down some network devices, line cards, or ports,
putting them into a deep sleep state. This directly reduces the
number of active devices at the physical level and significantly
lowers basic energy consumption.
Second, the green routing algorithm strategy integrating carbon
emission intensity. In the process of establishing or re-optimizing
paths for private line users, real-time or predicted "carbon emission
intensity" is introduced as a key metric, giving priority to low-
carbon paths powered by clean energy or with higher energy efficiency
ratios. This mechanism not only reduces the carbon footprint in a
single connection but also gradually forces old nodes with high
carbon emission factors to exit the core forwarding plane through
long-term traffic guidance, accelerating the iteration and update of
network infrastructure to high-energy-efficiency nodes.
The existing relevant research content in the IETF includes:
[I-D.many-lsr-power-group-02] proposes a mechanism for advertising
and managing power-groups using the IS-IS routing protocol. Its core
is to enable the controller to perceive the power consumption
dependencies of network hardware, support more intelligent traffic
engineering, and thereby achieve network energy conservation. This
mechanism can be well combined with the traffic tidal phenomenon,
concentrating traffic into several power groups and putting unloaded
components into sleep.
[I-D.petra-path-energy-api-02] defines a PETRA API that provides a
standardized network energy consumption query interface, allowing
users to send queries to the network to retrieve traffic-related
energy consumption and environment-derived metrics for specified
network paths. These metrics are computed by the network
infrastructure devices dynamically involved in the path. Through the
API, users can query and select forwarding paths with lower carbon
emissions.
[I-D.belmq-green-framework-10] proposes an architecture for energy
consumption collection and monitoring. The draft mentions an API
that enables external systems— such as upper-layer energy management
systems, carbon accounting platforms, and operational dashboards—to
query and retrieve energy consumption, energy efficiency metrics, and
associated metadata for devices or networks.
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[I-D.ietf-green-terminology-01] specifies the metrics applicable to
energy consumption assessment and provides a reference for terms and
parameters related to energy-efficient routing. Among these, the
Energy Efficiency Ratio (EER) is a key metric for evaluating the
energy conversion efficiency of networks, devices, or components,
which is fundamentally defined as the ratio of useful output to
energy input in an energy conversion process. The energy efficiency
ratio is a key parameter for private line energy consumption and
carbon emission intensity assessment.
[RFC9252] defines the fundamental architecture and operational
principles of Segment Routing (SR) and describes the SR network
programming model, which enables flexible network path control
through the definition of Segment Identifiers (SIDs). This document
focuses on path computation based on energy consumption information
and utilizes SR to implement energy-aware path control.
1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
[RFC2119] (Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997) and [RFC8174]
(Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key
Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017).
1.2. Terminology
Energy Efficiency/Energy Efficiency Ratio (EER): The energy
efficiency is expressed as the ratio between the useful output and
input of an energy conversion process of a network, device, or
component[I-D.ietf-green- terminology-01].
This ratio (i.e., Energy Efficiency Ratio, EER) is the throughput
forwarded by 1 watt (e.g., [I-D.cprjgf-bmwg-powerbench]).
2. Background
In the modern digital era, driven by the demand for sustainable
development and the need to reduce operational costs, network energy
consumption has become a core concern. Networks consume substantial
energy, leading to carbon emissions and environmental impacts.
Optimizing energy usage helps reduce their carbon footprint and
supports global efforts to combat climate change. Energy is a major
operational expense for network operators, and improving efficiency
directly lowers electricity costs, especially in large-scale
networks, resulting in significant financial savings. As network
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traffic grows exponentially, energy-efficient designs ensure
sustainable scalability without proportional increases in energy
consumption, which is essential for supporting future technologies
such as 5G, IoT, and cloud computing.
The source routing characteristics of SR make it a flexible,
scalable, and efficient networking technology. SR simplifies network
control, enables explicit path definition, and ensures compatibility
with existing technologies, which can meet the demands of modern
networks for traffic engineering, fault recovery, and scalability
while reducing complexity and overhead. Additionally, SR networks
support network slicing, allowing the creation of independent paths
for different service types.
SR networks can be utilized for energy-efficient path optimization in
large-scale networks and seamlessly integrate with existing IPv4/IPv6
infrastructures. By collecting energy consumption data from each
node and link, SR can plan energy-efficient paths based on routing
policies, thereby achieving the goal of reducing overall network
energy consumption.
The motivations for addressing energy consumption in SR networks
include, but are not limited to:
1. Through SR TE, traffic can be concentrated into a few device
components, providing a foundation for dynamic shutdown based on
traffic tides.
2. SR networks enable deterministic evaluation of energy consumption
and carbon emissions across different paths from source to
destination. Due to variations in the geographical locations and
construction timelines of Core Network Rooms housing forwarding
devices, there are significant differences in device energy
efficiency levels and the proportion of renewable (green)
electricity used. Leveraging the capabilities of SR networks, it
becomes possible to directly compare and assess the energy cost
and carbon footprint of alternative forwarding paths.
3. Energy consumption parameters
Energy consumption parameters include Energy Efficiency Ratio (EER),
renewable electricity usage ratio, carbon emission factor, etc.
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3.1. Energy Efficiency:
The energy efficiency metric EER is expressed in megabits per watt
(Mbit/W), representing the actual forwarding throughput achieved per
watt of power consumed. A higher value indicates better device
energy efficiency. This metric is typically derived from laboratory
testing and is distributed in the network as a static value.
For more details on the EER metric, please refer to
[I-D.ietf-green-terminology-01].
3.2. Renewable electricity usage ratio & carbon emission factor:
For carbon emission estimation of traffic traversing multi-hop,
multi-site paths with varying renewable electricity usage ratios
across different facilities, a per-segment accounting method SHOULD
be employed. For each segment corresponding to a facility along the
traffic path, carbon emissions associated with fossil fuel-based
electricity MUST be calculated by deducting the portion covered by
renewable energy.
The carbon emission calculation formula for a single segment is as
follows:
Cn = En x Fn x (1 - Rn)
where: Cn is the carbon emission of segment n (t CO2e), En is the
electricity consumption allocated to the traffic on segment n (kWh),
Fn is the grid average carbon emission factor for the region where
segment n is located (t CO2e/kWh), Rn is the renewable energy ratio
consumed at the facility of segment n.
The grid average carbon emission factor Fn indicates the carbon
intensity of the local power grid. A higher value of Fn implies a
higher share of fossil fuel-based electricity, a lower share of
renewable energy, and a higher environmental cost associated with
power consumption.
The grid carbon emission factor Fn is obtained from official regional
grid emission databases, and updated periodically (e.g., annually).
The renewable energy ratio Rn is provided per site/facility by the
operator's energy management system or carbon management platform,
based on actual renewable energy consumption and credible energy
attribute certificates. The network controller does not generate
these parameters but retrieves them via northbound interfaces or
local configuration.
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4. Private Line Carbon Accounting Mechanism
The computation framework for carbon accounting in SR networks
proposed in this document is as follows: A centralized controller
collects EER parameters from all nodes in the SR domain, and
retrieves the renewable energy ratio and carbon emission factor per
node from the energy management system and other related platforms.
When a path query is triggered via an external API (e.g., PETRA API),
the controller calculates the end-to-end energy consumption and
carbon emissions for candidate paths according to the source,
destination, and traffic volume. After the optimal path is selected
by the API caller, the controller deploys the selected path as an SR
Policy to the head-end node.
+------------------+
|Carbon Management |
+------/|\ |-------+
| | API(PETRA API)-Energy Consumption Information Query
Carbon Emissions | |
Energy Consumption | |
+-------| \|/------+
+--------|Network Controller| Energy Consumption and Carbon Emissions Calculation
| +------/|\ |-------+
SR-Policy | Power metrics | | Energy-aware Forwarding Path Optimization
| Collection | |
| | |
+-\|/-+ +---------| \|/---------+ +-----+
Handling |Head |---| Segment Routing |---|End |
behaviors |Point| | Network Domain | |Point|
| | | PE ----- P ------ PE | | |
+-----+ +-----------------------+ +-----+
Figure 1: Framework of Computing Energy Consumption path in SR
network
4.1. Energy Consumption Collection
The Energy Efficiency Ratio (EER) is distributed and collected within
the SR network domain through IGP protocol extensions. In cross-
domain scenarios, it can be advertised and collected using BGP-LS
(BGP Link-State) extensions.
The collection of energy consumption information between the SR
network domain and the network controller adopts standardized
methods, such as YANG, NETCONF, and SNMP.
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The renewable electricity usage ratio and carbon emission factor are
obtained by the controller from the carbon management platform.
4.2. Path Calculation Based on Energy Consumption
The network controller selects network paths based on the collected
energy consumption information and performs path computation
according to a specified policy. First, calculate N candidate paths
using traditional metrics such as bandwidth, delay, and packet loss
rate.
Then, evaluate the energy consumption and carbon emissions of each
path.
Finally, the controller returns the computed results—including both
energy and carbon metrics—to the upper-layer application via an API.
It is important to emphasize that carbon emission assessment is
critical, as the total power consumption of a path—derived from
traffic volume and device energy efficiency ratio (EER)—may not
accurately reflect its true environmental impact.
For example: Suppose the controller receives an API request
specifying a source address, destination address, and traffic volume,
and computes two candidate paths. Path A has a higher total power
consumption than Path B. However, because the data centers or nodes
along Path A use a significantly higher proportion of renewable
(green) electricity, the resulting carbon emissions—obtained by
converting the electricity consumed into CO2 equivalents using
location- and time-specific emission factors—are substantially lower
for Path A. In this case, despite its higher power draw, Path A
represents the environmentally preferable option with a lower overall
carbon footprint.
4.3. Issuance of Path
The network controller distributes the path to the head-end node.
This distribution can be performed using standard mechanisms such as
YANG, BGP, or PCEP.
The head-end node conducts network forwarding based on the
distributed SR Policy.
When using YANG, BGP, or PCEP, necessary expansions for the energy
consumption metric should be made.
5. Use Case
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5.1. Dynamic link shutdown
+------------------+
+--------|Network Controller|
| +------/|\ |-------+
SR-Policy | Power metrics | | Energy-aware Forwarding Path Optimization
| Collection | |
| | \|/
+-\|/-+ +----P1----P2----P3----+ +-----+
| | | | | | |
|Head |--- PE1 | PE2-------|End |
|Point| | | | |Point|
+-----+ +----P4----P5----P6----+ +-----+
Figure 2: Leveraging traffic tide patterns for dynamic shutdown
of network elements
As shown in the figure, there are multiple reachable forwarding paths
from the head node to the end node:
PE1-P4-P5-P6-PE2 and PE1-P1-P2-P3-PE2.
When night falls and the traffic proportion gradually decreases, TE
technology can be used to concentrate traffic onto one path and put
the other into deep sleep to reduce energy consumption.
5.2. Network Path Carbon Emission Assessment
+------------------+
|Carbon Management |
+------/|\ |-------+
| | API(PETRA API)-Energy Consumption Information Query
Carbon Emissions | |
Energy Consumption | |
+-------| \|/------+
+--------|Network Controller| Energy Consumption and Carbon Emissions Calculation
| +------/|\ |-------+
SR-Policy | Power metrics | | Energy-aware Forwarding Path Optimization
| Collection | |
| | \|/
| EER:100 Mbits/W
+-\|/-+ +---------P1-------+ +-----+
| | 100| |100 | |
|Head |--- PE1 PE2---|End |
|Point| | 200 Mbits/W | |Point|
+-----+ +---------P2-------+ +-----+
Figure 3: Network Path Carbon Emission Assessment
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As shown in the figure above, there are two paths from the head node
to the tail node: PE1 -> P1 -> PE2 and PE1 -> P2 -> PE2.
Among them, PE1, PE2, and P1 have the same energy efficiency
parameter of 100 Mbits/W, with a green power usage ratio of 50%.
Device P2 has an energy efficiency ratio (EER) of 200 Mbits/W and a
green power usage ratio of 10%.
At this time, an upper-layer application queries the optional paths
from the head node to the tail node, as well as their power
consumption and carbon emission costs, via an API.
The calculation process is as follows:
1. After the router devices distribute the parameters via IGP, they
synchronize the energy efficiency ratio parameter EER to the
network controller through BGP-LS (since EER is a static
parameter, it does not need to be flooded repeatedly).
2. The controller obtains the local power grid carbon emission
factor Fn of each node and the green power usage ratio Rn of the
core network equipment room where the node is located from the
carbon management platform.
3. The controller parses the source address, destination address,
and traffic volume from the parameters input via the API. Assume
the traffic volume is 2000 Mbits.
4. The controller calculates the optional paths:
* Path 1: PE1 -> P1 -> PE2
* Path 2: PE1 -> P2 -> PE2
5. The controller calculates the energy consumption and carbon
emission level for each segment of the optional paths. In this
example, the emission levels of Path 1 and Path 2 differ due to
P1 and P2:
* P1 has an energy efficiency ratio of 100 Mbits/W, so the power
consumption for 2000 Mbits traffic is 0.02 kW.The
corresponding carbon emission is: Cn = 0.02 x Fn x (1 - 0.5) =
0.01Fn
* P2 has an energy efficiency ratio of 200 Mbits/W, so the power
consumption for 2000 Mbits traffic is 0.01 kW.The
corresponding carbon emission is: Cn = 0.01 x Fn x (1 - 0.1) =
0.009Fn
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It can be seen from the above that even though P2 has better energy
efficiency at the device level, Path 1 has lower carbon emissions due
to its higher green power usage ratio.
6. IANA Considerations
The Flow Monitor Option Type should be assigned in IANA.
7. Security Considerations
TBD.
Acknowledgments
The authors would like to thank the following for their valuable
contributions of this document: TBD
References
Normative References
[I-D.cprjgf-bmwg-powerbench]
Pignataro, C., Jacob, R., Fioccola, G., and Q. Wu,
"Characterization and Benchmarking Methodology for Power
in Networking Devices", Work in Progress, Internet-Draft,
draft-cprjgf-bmwg-powerbench-05, 7 July 2025,
<https://datatracker.ietf.org/doc/html/draft-cprjgf-bmwg-
powerbench-05>.
[I-D.many-lsr-power-group-02]
Barth, C., Li, T., Beeram, V. P., and R. Bonica, "Using
IS-IS To Advertise Power Group Membership", Work in
Progress, Internet-Draft, draft-many-lsr-power-group-02,
25 January 2026, <https://datatracker.ietf.org/doc/html/
draft-many-lsr-power-group-02>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
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[RFC9252] Dawra, G., Ed., Talaulikar, K., Ed., Raszuk, R., Decraene,
B., Zhuang, S., and J. Rabadan, "BGP Overlay Services
Based on Segment Routing over IPv6 (SRv6)", RFC 9252,
DOI 10.17487/RFC9252, July 2022,
<https://www.rfc-editor.org/rfc/rfc9252>.
Informative References
[I-D.belmq-green-framework-10]
Claise, B., Contreras, L. M., Lindblad, J., Palmero, M.
P., Stephan, E., and Q. Wu, "Framework for Energy
Efficiency Management", Work in Progress, Internet-Draft,
draft-belmq-green-framework-10, 8 February 2026,
<https://datatracker.ietf.org/doc/html/draft-belmq-green-
framework-10>.
[I-D.ietf-green-terminology-01]
Chen, G., Boucadair, M., Wu, Q., Contreras, L. M., and M.
P. Palmero, "Terminology for Energy Efficiency Network
Management", Work in Progress, Internet-Draft, draft-ietf-
green-terminology-01, 13 February 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-green-
terminology-01>.
[I-D.petra-path-energy-api-02]
Rodriguez-Natal, A., Contreras, L. M., Muniz, A., Palmero,
M. P., Munoz, F., and J. Lindblad, "Path Energy Traffic
Ratio API (PETRA)", Work in Progress, Internet-Draft,
draft-petra-path-energy-api-02, 8 July 2024,
<https://datatracker.ietf.org/doc/html/draft-petra-path-
energy-api-02>.
Contributors
Shujun Hu
China Mobile
Email: hushujun@chinamobile.com
Authors' Addresses
Yisong Liu
China Mobile
Email: liuyisong@chinamobile.com
Changwang Lin
New H3C Technologies
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Email: linchangwang.04414@h3c.com
Ran Chen
ZTE Corporation
Email: chen.ran@zte.com.cn
Jinming Li
China Mobile
Email: lijinming@chinamobile.com
Luis M. Contreras
Telefonica
Email: luismiguel.contrerasmurillo@telefonica.com
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