Hybrid Energy Saving Mechanism for Transport Network
draft-chen-green-transport-energy-saving-00

GREEN                                                            X. Chen
Internet-Draft                                                      CMCC
Intended status: Standards Track                                 J. Zhou
Expires: 3 September 2026                                         J. Yan
                                                                     ZTE
                                                            2 March 2026

          Hybrid Energy Saving Mechanism for Transport Network
              draft-chen-green-transport-energy-saving-00

Abstract

   This document continues the transport network energy saving that
   harmonizes device-level autonomy with network-wide coordination.  By
   implementing control at hybrid both the device and network controller
   coordination, it enables dynamic, SLA-aware, and multi-layer energy
   optimization.

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

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Monitoring  . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Hybrid Both Device and Controller Coordination  . . . . . . .   3
     3.1.  Device-Centric Energy Saving  . . . . . . . . . . . . . .   4
     3.2.  Controller-Centric Energy Saving  . . . . . . . . . . . .   5
   4.  YANGs Considerations  . . . . . . . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
   6.  Informative References  . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   9

1.  Introduction

   This document presents transport network energy saving management
   framework that harmonizes device-level autonomy with network-wide
   coordination.  The framework is grounded in
   [I-D.belmq-green-framework] 's reference model and addresses the
   specific requirements identified in [I-D.ietf-green-use-cases]
   through practical mechanisms for multi-layer energy optimization.

   The framework is organized into two functionally distinct yet
   complementary layers that work in concert to achieve coordinated
   energy optimization:

   *  Device-Centric Energy Saving: The device-centric management
      encompasses individual network elements that execute real-time,
      localized energy adjustments based on local data collection and
      policies received from the network controller.  Device-centric
      management enables fast response to transient traffic conditions
      and maintains autonomous operation.

   *  Controller-Centric Energy Saving: The controller-level energy
      management provides centralized visibility, cross-layer analysis,
      and strategic policy formulation across the entire network domain.
      Controller-centric management performs long-term traffic
      prediction, assesses network-level risks, and provides a
      northbound interface to users for intuitive evaluation of energy-
      saving effects.

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2.  Monitoring

   Transport networks requires comprehensive, real-time, and granular
   measurements spanning physical, logical, and environmental domains to
   enable cross-layer correlation and coordinated optimization.

   *  Power Monitoring: Power monitoring is the foundation for
      understanding energy consumption patterns and identifying
      optimization opportunities.  Power monitoring encompasses
      measurements at multiple hierarchical levels within network
      elements, from component-level including boards, fans, and PSU to
      device-level chassis power.

   *  Traffic Monitoring: Traffic monitoring extends the framework to
      address transport network-specific requirements.  Understanding
      traffic characteristics across multiple layers is essential for
      correlating energy consumption with network utilization and for
      identifying temporal patterns that enable predictive optimization.
      This enables both real-time response to transient traffic changes
      and long-term trend analysis for strategic planning.

   *  Topology Monitoring: Topology monitoring provides the cross-layer
      visibility necessary for coordinated energy optimization.
      Transport networks span multiple protocol layers with complex
      interdependencies; understanding these relationships is critical
      for making energy-saving decisions that respect service
      requirements and network constraints.  Topology monitoring across
      both transport and IP layers should be used to capture relevant
      logical relationships, such as protection relationships, resource
      aggregation, and service-to-infrastructure mappings that support
      the formulation of energy optimization strategies.

3.  Hybrid Both Device and Controller Coordination

   Section 6.1 of [I-D.belmq-green-framework] discuss the implementation
   focus and where intelligence resides.  The transport network uses the
   hybrid approach which need device capabilities and controller
   coordination.

   Transport network device must independently manage its energy saving
   no matter DCN is available for local real-time process.  It needs
   local algorithms, minimal controller dependency, autonomous
   operation.  Secondly, the device-centric performs traffic prediction,
   quickly responds to short-term traffic changes, formulates
   strategies, and executes actions.

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   On the other side, the controller-centric energy saving performs
   long-term traffic prediction based on network topology resources,
   assesses network-level risks, provides a northbound interface to
   users, and enables visualized and intuitive evaluation of energy-
   saving effects.

   Depending on the scenario, inference time, accuracy, and other
   factors, different intelligent algorithms are deployed on the
   controller and device to intelligently predict long and short cycles
   and burst traffic.  This allows the controller to accurately predict
   long-term changes in services and devices to accurately predict
   short-term burst traffic, thereby adjusting the equipment operating
   status in advance and avoiding service disruption.

3.1.  Device-Centric Energy Saving

   This allows devices to make local decisions on resource scheduling
   based on real-time, node-local data/information collection, enabling
   faster reaction to transient traffic conditions though on-device
   analysis.

   1.  Data collection

       *  Sensors continuously gather real-time power and energy-related
          metrics, including chip, port, board, fan, and chassis power
          consumption, as well as device zone temperature and
          instantaneous traffic load.

   2.  Analysis

       *  An embedded processing unit applies lightweight algorithms to
          model the relationship between local load and power
          consumption.  It performs short-term traffic trend prediction,
          evaluates energy-saving strategies through simulation, and
          supports cross-layer command coordination.  Conditions are
          continuously assessed against configured energy-saving
          policies.

   3.  Simulation and Verification

       *  The simulation model describes the relationships between
          parameters of device and relationships between devices
          themseleves.  It use the data from scenario and energy-saving
          scheme to simulate and verfify the consumption information
          after executing energy-saving scheme.  Based on the power
          consumption information, the feasibility of energy-saving
          scheme is tested and determined.

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   4.  Control and Execution

       *  Devices dynamically switch the power state of the components
          (e.g., ports, line cards, switch cards, chassis) based on
          local traffic load, in accordance with policies received from
          the device controller.  For instance, when predicted traffic
          falls below a predefined threshold, the system sequentially
          initiates energy-saving actions, such as placing boards into
          sleep mode and intelligently adjusting fan speeds.

       *  Supported power states include deep sleep, light sleep, normal
          operation, and power-off.  Deep sleep maintains only essential
          core links, substantially reducing energy consumption during
          idle periods.  Light sleep can satisfy hitless wake-up from
          sleep modes to ensure zero service impact, particularly for
          high-priority services.

3.2.  Controller-Centric Energy Saving

   This network-level energy management operates from a network
   controller platform, providing a holistic view and strategic control.
   Unlike device-local management, its role is primarily one of
   coordination, optimization, and assurance across the multi-layer
   network.

   1.  Data collection

       The controller ingests and correlates telemetry from all managed
       devices, building a holistic network model that spans real time
       power consumption, topology, and traffic state.

       *  Transport Layer Topology: Logical link information and
          resource status from both the optical layer (L0) and
          electrical layer (L1/L2).

       *  IP Layer Topology: Logical links, protection relations and
          routing adjacencies from the IP layer (L3).  For instance,
          protection paths may carry extra traffic under normal
          conditions but must be reserved for failover scenarios.  This
          integrated view allows the network controller to assess risks,
          such as extended wake-up delays from deep sleep modes, that
          could impact service performance during protection switching
          or other reactive scenarios.

   2.  Analysis

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       *  Leveraging AI/ML and analytical engines, the controller
          performs predictive traffic and load forecasting.  It
          identifies optimization opportunities through cross layer
          correlation.  It also can simulates the potential impact of
          different energy management strategies before deployment.

       *  Cross-layer Coordination: The controller translates high-level
          strategies into specific, synchronized actions for both
          transport and IP layers to ensure service continuity.  For
          example, before putting a transport node to sleep, it
          coordinates with the IP layer to reroute traffic away from
          that node.

   3.  Control and Execution

       The central controller acts as the brain for network-level energy
       optimization.  Its key functions include:

       The controller analyzes historical and real-time traffic data to
       predict future load patterns.  Based on these predictions and
       service SLAs, it generates holistic energy-saving strategies,

       *  Computing paths for traffic migration to consolidate services
          onto fewer network elements.

       *  Instructing idle or underutilized devices to enter low-power
          states (e.g., deep sleep for best-effort services, light sleep
          for premium services).  These policies are then dispatched to
          devices.

4.  YANGs Considerations

   The implementation of the hybrid device-centric and controller-
   centric energy optimization requires standardized data models for
   representing energy-related information, policies, and control
   mechanisms.  This section discusses the YANG data model
   considerations for this implementation.

   The framework defines information flows between devices and
   controllers:

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                                  User
                                   ^
                                   |
                                   |
                                   v
   +------------------------------------------------------------------+
   |                                                                  |
   |                 Transport Network Controller                     |
   |                                                                  |
   +------------------------------------------------------------------+
                  ^                                   ^
                  |                                   |
               Monitoring                   Energy-Saving Strategy
                  |                                   |
                  v                                   v
   +----------------------------+        +----------------------------+
   |                            |        |                            |
   |  Transport Network Element |<------>|  Transport Network Element |
   |                            |        |                            |
   +----------------------------+        +----------------------------+

            Figure 1: Transport Network Energy Saving Framework

   Devices report operational data including power measurements, traffic
   characteristics, device status, and multi-granularity aggregated data
   to the controller.  Controllers distribute energy-saving policies,
   SLA constraints, cross-layer control commands, and configuration
   updates to devices.

   To address this hybrid coordination, the following YANG
   considerations should be evaluated:

   1.  Controller and Users: YANG models for northbound interfaces
       enabling users to configure energy-saving objectives, view
       optimization results, and monitor energy consumption.

   2.  Controller and Devices: YANG models for southbound interfaces
       enabling the controller to distribute policies, receive
       telemetry, and issue control commands to devices.

   3.  Device and Device: Peer-to-peer interactions between devices to
       support cross-layer coordination and local optimization.  This
       may involve protocol or signaling extensions, such as capability
       advertisement, energy-saving status synchronization, or the
       notifications of energy-saving policies, which can guide other
       devices to perform operations or provide information to the other
       devices.

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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.  Informative References

   [I-D.belmq-green-framework]
              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]
              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>.

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   [I-D.ietf-green-use-cases]
              Stephan, E., Palmero, M. P., Claise, B., Wu, Q.,
              Contreras, L. M., Bernardos, C. J., and X. Chen, "Use
              Cases for Energy Efficiency Management", Work in Progress,
              Internet-Draft, draft-ietf-green-use-cases-01, 22 January
              2026, <https://datatracker.ietf.org/doc/html/draft-ietf-
              green-use-cases-01>.

Authors' Addresses

   Xinyu Chen
   China Mobile
   No.32 Xuanwumen west street
   Beijing
   100053
   China
   Email: chenxinyuyjy@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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