



SPRING                                                            Y. Liu
Internet-Draft                                              China Mobile
Intended status: Standards Track                                  C. Lin
Expires: 16 August 2026                             New H3C Technologies
                                                                 R. Chen
                                                         ZTE Corporation
                                                                   J. Li
                                                            China Mobile
                                                         L. M. Contreras
                                                              Telefonica
                                                        12 February 2026


     Computing Energy Consumption Path in Segment Routing Networks
            draft-liu-spring-sr-policy-energy-efficiency-04

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 including energy
   consumption and carbon emissions on network paths.  It covers the
   procedures for data collection, path computation and issuance, and
   also specifies the implementation considerations for the data plane
   in both Multiprotocol Label Switching Segment Routing (MPLS SR) and
   IPv6 Segment Routing (SRv6) networks.

Status of This Memo

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   This Internet-Draft will expire on 16 August 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.



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   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
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
     1.2.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Energy consumption parameters . . . . . . . . . . . . . . . .   5
   4.  Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.1.  Energy Consumption Collection . . . . . . . . . . . . . .   7
     4.2.  Path Calculation Based on Energy Consumption  . . . . . .   7
     4.3.  Issuance of Path  . . . . . . . . . . . . . . . . . . . .   7
   5.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   8
     5.1.  Network Path Carbon Emission Assessment . . . . . . . . .   8
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .   9
   References  . . . . . . . . . . . . . . . . . . . . . . . . . . .   9
     Normative References  . . . . . . . . . . . . . . . . . . . . .   9
     Informative References  . . . . . . . . . . . . . . . . . . . .  10
   Contributors  . . . . . . . . . . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   The importance of energy consumption in modern networks is becoming
   increasingly evident.  In addition to techniques such as device sleep
   modes and dynamic shutdowns, network technologies can also be
   leveraged to steer traffic toward more energy-efficient devices and
   paths, thereby reducing the energy consumption of network
   communications.

   [I-D.petra-path-energy-api-02] The PETRA API defines a standardized
   network energy query interface that allows queries to be sent to the
   network to retrieve traffic-related energy consumption and
   environmental-derived metrics for a specified network path.  These
   metrics are computed by the network infrastructure elements
   dynamically involved in the path.  The API only specifies a unified
   query interaction protocol and does not define the actual computation
   logic for these metrics.



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   [I-D.belmq-green-framework-10] 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.  The PETRA API can be
   used to evaluate traffic-related energy consumption and carbon
   emissions for any source-to-destination node pair.

   [I-D.ietf-green-terminology-00] specifies the metrics applicable to
   energy consumption assessment and provides a reference for the
   terminology and parameters used in 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.  It is fundamentally defined as the ratio of useful
   output to energy input in an energy conversion process, and can be
   used to assess energy consumption or carbon emissions.

   [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-00].

   This ratio (i.e., Energy Efficiency Ratio, EER) is the throughput
   forwarded by 1 watt (e.g., [I-D.cprjgf-bmwg-powerbench]).








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2.  Background

   In the modern digital era, network energy consumption has become a
   critical focus, driven by the growing demand for sustainable
   practices and the need to reduce operational costs.  Networks consume
   substantial energy, leading to carbon emissions and environmental
   degradation.  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 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.  By simplifying
   network control, enabling explicit path definition, and ensuring
   compatibility with existing technologies, SR meets 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 enables the planning of 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.  Reducing energy consumption in network communications by
       selecting energy-efficient paths and leveraging energy-related
       information associated with SR paths and policies.

   2.  Allowing the source node or controller/PCE to use energy
       consumption metrics as constraints and optimization criteria for
       path computation, thereby optimizing the routing of network
       communications.

   3.  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



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       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 EER, green energy usage ratio,
   carbon emission factor, etc.

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-00].

2.  Renewable electricity usage ratio & carbon emission factor

    For carbon emission estimation of traffic traversing multi-hop,
    multi-site paths with varying renewable energy 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 for a
    single segment is computed as:

Cn = En × Fn × (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.



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    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.

4.  Mechanism

   The proposed energy consumption and carbon emission aware path
   computation framework for SR networks is described 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 emission factor
 +------------------+                  |     API(PETRA API)-Energy Consumption Information Query
 |Carbon Management |----------|       |
 +------------------+         \|/      |
                             +--------\|/-------+
                    +--------|Network Controller| Energy Consumption and Carbon Emissions Calculation
                    |        +--------/|\-------+
                    |                  |
                SR-Policy        EER 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







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4.1.  Energy Consumption Collection

   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 protocol
   extensions via 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.

   The green power 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, it calculates N candidate
   paths using traditional metrics such as bandwidth, delay, and packet
   loss rate.  Then, it evaluates the energy consumption and carbon
   emissions for each of these paths.  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 path to the head end.  This
   distribution can be performed using standard mechanisms such as YANG,
   BGP or PCEP.  The head end then conducts network forwarding based on
   the distributed SR-Policy.  When using YANG, BGP and PCEP, necessary
   expansions for the energy consumption metric should be made.




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5.  Use Case

5.1.  Network Path Carbon Emission Assessment

                 carbon emission factor
    +------------------+                  |     API Query
    |Carbon Management |----------|       |
    +------------------+         \|/      |
                                +--------\|/-------+
                       +--------|Network Controller|
                       |        +------------------+
                       |
                       |
                       |            EER:100 Mbits/W
                    +-\|/-+     +---------P1-------+    +-----+
                    |     |  100|                  |100 |     |
                    |Head |--- PE1                PE2---|End  |
                    |Point|     |    200 Mbits/W   |    |Point|
                    +-----+     +---------P2-------+    +-----+

              Figure 2: Use Bit0 For Out-of-order Measurement

   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.






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   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 × Fn × (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 × Fn × (1 − 0.1) =
          0.009Fn

   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






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   [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>.

   [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>.

   [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-00]
              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-00, 18 November 2025,
              <https://datatracker.ietf.org/doc/html/draft-ietf-green-
              terminology-00>.

   [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>.



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Contributors

   Shujun Hu
   China Mobile
   Email: lijinming@chinamobile.com


Authors' Addresses

   Yisong Liu
   China Mobile
   Email: liuyisong@chinamobile.com


   Changwang Lin
   New H3C Technologies
   Email: linchangwang.04414@h3c.com


   Ran Chen
   ZTE Corporation
   Email: xiao.min2@zte.com.cn


   Jinming Li
   China Mobile
   Email: lijinming@chinamobile.com


   Luis M. Contreras
   Telefonica
   Email: luismiguel.contrerasmurillo@telefonica.com



















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