



GREEN                                                            X. Chen
Internet-Draft                                                   M. Wang
Intended status: Standards Track                                    CMCC
Expires: 8 January 2026                                          J. Zhou
                                                                  J. Yan
                                                                     ZTE
                                                             7 July 2025


                   Transport Network Level Use Cases
               draft-chen-green-transport-network-ucs-00

Abstract

   With the continuous growth of business volume, the transmission rate
   and number of network elements have increased sharply, and energy
   consumption of transport network has increased accordingly.  Rising
   power costs due to significant energy consumption in transport
   networks necessitate energy-saving measures.  To address this,
   adjusting energy consumption strategies according to different
   service requirements to optimize efficiency, ensuring quality while
   eliminating waste.  Furthermore, regular network optimization and
   energy efficiency assessments enhance equipment performance and
   extend lifespan, thereby controlling long-term operational costs.
   Integrating energy-saving concepts into transport network operations
   proactively supports sustainable development.

   This document presents two transport network level GREEN use cases,
   aiming to facilitate discussions within the GREEN Working Group on
   the potential benefits, challenges, and requirements.  The use cases
   follow a structured template that is proposed for all GREEN use
   cases, ensuring consistency and comparability across different
   scenarios.

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   material or to cite them other than as "work in progress."



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   This Internet-Draft will expire on 8 January 2026.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Transport Network Level Energy Optimization . . . . . . . . .   3
     2.1.  Use case description  . . . . . . . . . . . . . . . . . .   3
     2.2.  GREEN Specifics . . . . . . . . . . . . . . . . . . . . .   5
     2.3.  Requirements for GREEN  . . . . . . . . . . . . . . . . .   5
   3.  Diversified Service Assurance with GREEN  . . . . . . . . . .   5
     3.1.  Use case description  . . . . . . . . . . . . . . . . . .   6
     3.2.  GREEN Specifics . . . . . . . . . . . . . . . . . . . . .   6
     3.3.  Requirements for GREEN  . . . . . . . . . . . . . . . . .   6
   4.  Deployment Considerations . . . . . . . . . . . . . . . . . .   7
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   6.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .   7
   7.  Informative References  . . . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   The transport network consists of numerous transport network
   elements, whose architecture directly affects energy consumption and
   network performance.  Meanwhile, different traffic patterns lead to
   varying network element loads.  The traffic model guides dynamic
   adjustments of network element operations to optimize energy
   utilization, ensuring that energy consumption scales proportionally
   with actual traffic demands, thereby achieving efficient energy
   saving.

   On the one hand, regularly network optimization and conducting energy
   efficiency assessments and optimization can enhance equipment
   performance and extend their lifespan, thereby effectively
   controlling costs in long-term operations.  Against the backdrop of



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   increasingly tight energy resources and stricter environmental
   requirements, integrating energy-saving concepts into the operation
   of transport network is not only a factor in reducing operating costs
   but also a proactive response to the sustainable development.

   On the other hand, as services continue to be assured in the
   transport network, the energy consumption of equipment accumulates
   over time, which significantly impacts operating costs in the long
   run.  Through refined energy-saving strategies, energy efficiency can
   be optimized without compromising service quality.  By employing
   intelligent traffic management to dynamically adjust the energy
   consumption of equipment based on dynamic service demands, not only
   can the efficient transport services be assured, but unnecessary
   energy waste can also be avoided.

   This document presents two use cases related to transport network,
   aiming to facilitate discussions within the GREEN Working Group on
   the potential benefits, challenges, and requirements.  The use cases
   follow a structured template that is proposed for all GREEN use cases
   [I-D.stephan-green-use-cases], ensuring consistency and comparability
   across different scenarios.

2.  Transport Network Level Energy Optimization

2.1.  Use case description

   The transport network consists of numerous devices, and its planning
   directly affects energy consumption and network performance.
   Different traffic patterns lead to differences in device loads, and
   from the perspective of the entire network, traffic is unevenly
   distributed.  Therefore, it's crucial to develop a comprehensive
   network-level energy optimization strategy in view of dynamic traffic
   patterns and device capabilities in transport network.

   This use case focuses on mid-to-long-term, strategic network energy
   efficiency optimization for operators.  That is, through traffic
   prediction, traffic model optimization, traffic scheduling, and
   adjustment of device operating status, and energy consumption is
   positively correlated with actual traffic demand, thereby achieving
   load balancing and resource utilization optimization, improving the
   energy-saving potential of network elements, and achieving efficient
   energy saving.









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     +---------------------------------------------------------------+
     |                 Transport Network Controller                  |
     |                                                               |
     |   Traffic Prediction  -> Network Optimization -> Evaluation   |
     +---------------------------------------------------------------+
                    ^                                   |
                    |                                   |
                Monitoring                   Energy-Saving Strategy
                    |                                   |
                    |                                   v
     +---------------------------------------------------------------+
     |                                                               |
     |                   Transport Network Element                   |
     |                                                               |
     +---------------------------------------------------------------+

       Figure 1: Network-level GREEN framework for transport network

   As shown in the figure above, network-level GREEN is located on the
   controller for centralized policy generation and global scheduling,
   and the network elements perform specific energy-saving operations at
   the local.

   First, network-level traffic prediction is performed based on
   historical traffic data obtained from the network.  The transport
   network elements process the original per-second measurement data and
   aggregates it into 15-minute time intervals to report to the
   controller.

   Secondly, based on the traffic monitoring from network elements, the
   controller captures the long-term regularity and short-term burst
   characteristics of network-level traffic through the neural network
   model in advance.  The traffic prediction model embedded in the
   controller generates updated predictions to achieve near real-time
   perception.  Based on the traffic load prediction results, the
   controller identifies high-load network elements (NEs) and idle NEs.
   The controller then formulates energy-saving traffic migration
   network optimization strategies.

   Finally, by migrating traffic from high-load NEs to idle NEs, some
   switching resources of high-load NEs can be released to enter a
   dormant state, saving power consumption; while idle NEs can carry
   more traffic with the support of the existing switching matrix
   without increasing power consumption, thereby reducing the overall
   energy consumption of the transport network.






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2.2.  GREEN Specifics

   1.  Network Level Monitoring

       *  The transport network elements process raw per-second traffic
          measurements, aggregating them into 15-minute intervals for
          reporting to the controller.

       *  The controller performs network-level traffic prediction based
          on historical.  This capability captures both long-term
          regular "tides" and the short-term "burst" characteristics.
          Based on traffic prediction results, energy-saving
          opportunities are able to be identified.  (e.g. This
          capability allows for the detection of significant reductions
          in traffic within a specific time window, thereby enabling the
          devices to transition into a sleep/lower-power state).

   2.  Centralized Strategy Generation and Global Scheduling

       *  The controller identifies high-load and idle network elements
          based on results of traffic prediction and historical data,
          and develops traffic migration and optimization strategies.

       *  By migrating the traffic from high-load NEs to idle NEs, some
          fabric resources are freed to sleep for power saving, while
          the idle NEs can hold more traffic without power increasing
          supported by the existing switch fabrics at work.

2.3.  Requirements for GREEN

   *  The controller has the ability to display energy consumption per
      transport network element at specified time granularity.

   *  The controller has the ability to perform accurate network-level
      traffic prediction, capturing both long-term regularities and
      short-term bursts, using historical traffic data.

   *  The controller has the capability to generate GREEN optimization
      strategies including the traffic migration methods based on
      traffic forecasts, operating on defined planning cycles.

3.  Diversified Service Assurance with GREEN









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3.1.  Use case description

   The transport network handles massive optical signals and fabric
   switching, which requires high bandwidth and low latency, and also
   incurs significant energy consumption.  In order to efficiently
   manage the transport network, it's necessary to integrate energy
   saving factors into service assurance.  This use case aims to achieve
   differentiated energy saving without affecting the quality of
   service.  Specifically, services with general service quality
   requirements try to use low-power devices and links; services with
   high service quality requirements use relatively safe energy-saving
   strategies to reduce power consumption while maintaining service
   requirements.

3.2.  GREEN Specifics

   *  Service assurance with energy efficiency: Energy efficiency
      factors can be taken into account in processes during service
      provisioning, in combination with the client's SLA and available
      underlying hard isolation resources.  The transport network
      element reports its energy consumption.  The controller evaluates
      the possible energy consumption during the service lifecycle.

   *  Control and Management: Local automatic energy saving can be
      processed on the network element, if enabled.  It identifies
      power-consuming components that can be turned off or reduced based
      on configuration and traffic information.  This may depend on the
      energy-saving object, such as the PHY, link, etc.  If the link is
      considered, deactivate the link or reduce the bit rate supported
      by link during periods of low demand are possible approaches for
      energy saving.  This may also needs the coordination between both
      the digital layers and the media layer.

3.3.  Requirements for GREEN

   *  The transport network element has the ability to measure and
      report its energy consumption.

   *  The controller has the capability to consider energy efficiency
      factor in the multi-layer resource allocation during service
      assurance lifecycle.

   *  The network element capability interacted between controller and
      transport network element must be considered, especially the
      energy-saving object, and the energy-saving capability.






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4.  Deployment Considerations

   In the field trial involving 1966 commercial network elements traffic
   data prediction, the network-level optimization strategy provides a
   power reduction of 7.7% for the whole network and up to 20.8% for the
   single network element node with service quality guaranteed.  This
   document fully verifies the potential of network-level energy-saving
   technology, demonstrating an effective approach for sustainable
   large-scale transport network operations.

5.  Security Considerations

   A general principle is that the more significant the energy savings,
   the slower the module response time and the longer the wake-up delay,
   which may impact service performance.

   To address this, the following items should be considered:

   1.  Power state configuration aligned with service tolerance: During
       low-traffic periods (e.g., nighttime), idle line cards/standby
       main control units can enter deep sleep mode for maximum energy
       savings.  During peak hours (e.g., daytime), a light sleep mode
       should be adopted to enable faster wake-up and minimize service
       disruption.

   2.  Resource reservation for reliable energy efficiency: In the
       transport network, the total bandwidth utilization of a network
       network element is primarily determined by the aggregate traffic
       across its ports.  However, in practice, the available capacity
       cannot be entirely assigned to user traffic, as a portion of the
       bandwidth must be reserved for protection switching, rerouting
       operations and control plane overhead.  It ensures the network
       reliability during network anomalies or congestion events.

   So redundant resources should be reserved to accommodate scenarios
   like protection switching at failure cases.  This guarantees service
   reliability while maintaining energy-saving benefits.

6.  Acknowledgments

   TBD.

7.  Informative References

   [I-D.stephan-green-use-cases]
              Stephan, E., Palmero, M. P., Claise, B., Wu, Q.,
              Contreras, L. M., and C. J. Bernardos, "Use Cases for
              Energy Efficiency Management", Work in Progress, Internet-



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              Draft, draft-stephan-green-use-cases-01, 16 May 2025,
              <https://datatracker.ietf.org/doc/html/draft-stephan-
              green-use-cases-01>.

Authors' Addresses

   Xinyu Chen
   China Mobile
   No.32 Xuanwumen west street
   Beijing
   100053
   China
   Email: chenxinyu@chinamobile.com


   Minxue Wang
   China Mobile
   No.32 Xuanwumen west street
   Beijing
   100053
   China
   Email: wangminxue@chinamobile.com


   Jin Zhou
   ZTE Corporation
   Email: zhou.jin6@zte.com.cn


   Jinjie Yan
   ZTE Corporation
   Email: yan.jinjie@zte.com.cn



















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