Hybrid Energy Saving Mechanism for Transport Network

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."¶

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. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised BSD License.¶

Table of Contents

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.¶

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.¶

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.¶

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:¶

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

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.¶

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.¶

Authors' Addresses