Computing Energy Consumption Path in Segment Routing Networks

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Table of Contents

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.¶

[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.¶

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 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.¶

   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 |   |     |
                 +-----+   +-----------------------+   +-----+

5. Use Case

              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-------+    +-----+

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

Contributors

Authors' Addresses