Green Challenges in Computing-Aware Traffic Steering (CATS)
draft-wang-cats-green-challenges-07

cats                                                             J. Wang
Internet-Draft                                                     Y. Fu
Intended status: Informational                              China Mobile
Expires: 27 June 2026                                              C. Li
                                                     Huawei Technologies
                                                        24 December 2025

      Green Challenges in Computing-Aware Traffic Steering (CATS)
                  draft-wang-cats-green-challenges-07

Abstract

   As mobile edge computing networks sink computing tasks from cloud
   data centers to the edge of the network, tasks need to be processed
   by computing resources close to the user side.  Therefore, CATS was
   raised.  Reducing carbon footprint is a major challenge of our time.
   Networks are the main enablers of carbon reductions.  The
   introduction of computing dimension in CATS makes it insufficient to
   consider the energy saving of network dimension in the past, so the
   green for CATS based on network and computing combination is worth
   exploring.  This document outlines a series of challenges and
   associated research to explore ways to reduce carbon footprint and
   reduce network energy based on CATS.

Status of This Memo

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   This Internet-Draft will expire on 27 June 2026.

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   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   3
   3.  Challenges  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Computing Resource Energy Consumption Modeling  . . . . .   3
     3.2.  Joint Optimization of Computing and Network . . . . . . .   4
     3.3.  Service Experience Guarantee  . . . . . . . . . . . . . .   4
     3.4.  Energy Consumption of Other Equipment . . . . . . . . . .   4
     3.5.  Evaluation of Computing Equipment Energy Consumption
           Performance . . . . . . . . . . . . . . . . . . . . . . .   4
     3.6.  Using of Green Energy . . . . . . . . . . . . . . . . . .   5
     3.7.  How to Realize the Accurate Control of Energy
           Consumption . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  Observation . . . . . . . . . . . . . . . . . . . . . . . . .   5
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   6
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   7
   9.  Informative References  . . . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   With the continuous development and progress of the Internet, a large
   amount of computing resources is required to complete data
   processing.  In order to disperse the pressure of cloud data centers,
   computing power gradually moves from the center to the edge, forming
   scattered computing resources in mobile networks.  In order to make
   full use of scattered computing resources and provide better
   services, Computing-Aware Traffic Steering (CATS) is proposed to
   support steering the traffic among different edge sites according to
   both the real-time network and computing resource status as mentioned
   in [I-D.ietf-cats-usecases-requirements].  It requires the network to
   be aware of computing resource information and select a service
   instance based on the joint metric of computing and networking.

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   Green has become a global topic.  The United Nations and the vast
   majority of governments agree that climate change and the need to
   curb greenhouse gas emissions are the major challenges of our time.
   Therefore, improving energy efficiency and reducing electricity
   consumption are becoming increasingly important for society and many
   industries.  The networking industry is no exception.  The IETF
   conducted a study on the energy costs of the IETF meeting three times
   a year.  The results showed that it was found that 99% of energy
   consumption came from air travel.

   In addition, there are several papers that discuss green networks,
   and some work [I-D.cx-green-ps] summarizes the energy-saving
   possibilities that exist in the network.  However, there is no
   discussion of joint optimization of green and energy savings with
   computing and networking.  Therefore, this document outlines a series
   of challenges and related research to explore ways to reduce carbon
   emissions and reduce network energy based on CATS.

2.  Definition of Terms

   Computing-Aware Traffic Steering (CATS):  Aiming at computing and
     network resource optimization by steering traffic to appropriate
     computing resources considering not only routing metric but also
     computing resource metric.

   Service:  A monolithic functionality that is provided by an endpoint
     according to the specification for said service.  A composite
     service can be built by orchestrating monolithic services.

   Service instance:  Running environment (e.g., a node) that makes the
     functionality of a service available.  One service can have several
     instances running at different network locations.

3.  Challenges

   Considering energy savings in CATS creates challenges in the
   following aspects

3.1.  Computing Resource Energy Consumption Modeling

   Computing resource status is considered in Cats, so it is necessary
   to research the modeling of computing resource energy consumption in
   order to save energy.  The energy consumption of the equipment is
   different when the load is different.  For example, the energy
   efficiency of equipment is different when it is not loaded or at full
   load.  Therefore, it is also a challenge to consider which factors to
   consider when modeling the energy consumption of computing resources.

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3.2.  Joint Optimization of Computing and Network

   The magnitude of computing energy consumption may differ from the
   magnitude of network energy consumption.  Therefore, when computing
   and network are jointly optimized, how to weigh the two in joint
   optimization becomes a challenge.  When the computing energy
   consumption is large enough, the impact of network energy consumption
   on the joint optimization results is negligible.

3.3.  Service Experience Guarantee

   The service experience that takes energy saving factors into account
   in CATS is distinct from the service experience that does not
   consider energy saving factors.  The implementation of energy
   conservation may come with sacrifices in user service experience.
   Users have limitations on factors such as latency when making
   requests.  Therefore, when conducting joint optimization, how to
   guarantee user service experience while conserving energy is also a
   challenge.

3.4.  Energy Consumption of Other Equipment

   The computing resources may be in the data center, edge computing
   nodes or others.  In order to ensure the normal operation of network
   and computing equipment, the source of energy consumption is not only
   the equipment itself, but also some other equipment, such as :

   Cool equipment : computing resources will emit heat into the air
   during operation.  When the temperature is too high, the operation of
   the equipment will be affected.  So refrigeration is required to
   reduce the temperature of the equipment to ensure that the equipment
   operates at a higher performance.

   The normal running of computing resources are inseparable from the
   support of refrigeration equipment and other equipment.  Therefore,
   when performing joint optimization of network and computing, the
   energy consumption generated by equipment other than network
   equipment and computing equipment should also be considered.

3.5.  Evaluation of Computing Equipment Energy Consumption Performance

   The energy efficiency level requirements of computing equipments can
   also be considered when performing traffic steering.  Since there is
   no standardized definition of computing energy efficiency for
   different computing equipments.  Therefore, it is difficult to
   consider the computing energy efficiency level of computing
   equipments when traffic steering.

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3.6.  Using of Green Energy

   Green energy, also known as clean energy, refers to energy that does
   not emit pollutants and can be directly used for production and daily
   life, including nuclear energy and renewable energy.  Renewable
   energy refers to energy sources that can be regenerated from raw
   materials, such as hydroelectric power, wind power, solar energy,
   bioenergy (biogas), geothermal energy (including geothermal and water
   sources), and tidal energy.  Renewable energy does not have the
   possibility of energy depletion, therefore, the development and
   utilization of renewable energy are increasingly valued by many
   countries, especially those with energy shortages.

   The development of green CATS cannot be separated from the use of
   green energy.  Although the current use of green energy in network
   devices has a certain scale, the industry's consideration of the
   consumption of green energy for equipment in traffic steering is
   incomplete and further research is needed.

   Additionally, the implementation of green energy requires
   collaboration with electricity suppliers through Energy Efficiency
   Management System to facilitate traffic routing.  Energy Efficiency
   Management System are used to centrally manage energy efficiency
   information for network and computing equipment, supporting the
   calculation or monitoring of their energy efficiency.  When multiple
   target computing power providers exist, priority should be given to
   those with a higher proportion of green energy for service provision.

3.7.  How to Realize the Accurate Control of Energy Consumption

   At present, the data center's control of energy consumption is based
   on monitoring the overall energy consumption of regional equipment.
   However, it has not yet achieved accurate control of equipment within
   the region.  While assessing the energy consumption of equipment in
   the overall region can help to some extent in controlling energy
   consumption, it does not provide accurate enough feedback on the
   actual situation of the equipment.

   In the future, there is a need to further explore fine-grained
   control of energy consumption in order to address scenarios such as
   AI training, which requires a significant amount of energy.

4.  Observation

   Recently, the document [I-D.cx-opsawg-green-metrics] gives some green
   networking metrics for network instrumentation to optimize the energy
   efficiency of the network.  It divides the green metrics into four
   categories according to the subject of the metrics, as follows:

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   At the device/equipment level: The author considers three factors.
   The first are energy consumption metrics.  Some of these metrics
   could be provided by the data sheet that comes with the device or
   could be measured simply in a lab, such as power consumption when
   idle, power consumption when fully loaded, power consumption at
   various loads and so on.  The others are not fixed and need to be
   accounted according to the actual operation of the network equipment,
   such as current power consumption/kB (or gB), current power
   consumption/packet, power drawn since system started for the past
   minute and so on.  The second is green metrics beyond energy
   consumption, Wich is related to the power source of the device and
   the environment in which the device is located.  The third is related
   to network instrumentation virtualization.  Nowadays, network
   instrumentation could be virtualized and hosted (for example) in data
   centers.

   At the flow level: These metrics are related to flows, such as
   amortized energy consumed over the duration of the flow and
   Incremental energy consumed over the duration of the flow.

   At the path level: These metrics can evaluate the energy consumption
   of paths and optimize these paths so that the overall footprint is
   minimized.  The author gives some candidate metrics, such as energy
   rating of a path, current power consumption across a path and
   incremental power for a packet over a path.

   At the network level: These metrics can reflect the energy usage of
   the entire network.

5.  Conclusion

   This document highlights the green challenges in Cats and summarizes
   the latest IETF work which is associated with green networking.  As
   is well known, Cats not only considers network resource status, but
   also computing resource status.  Therefore, energy consumption
   research of Cats can also consider both network and computing energy
   consumption from the device/equipment, path and network level.

6.  Security Considerations

   TBD.

7.  IANA Considerations

   TBD.

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8.  Acknowledgements

   The authors would like to thank thank Adrian Farrel and Peng Liu for
   their valuable suggestions to this document.  Additionally, the
   authors would also like to thank Alexander Clemm and Lijun Dong for
   their related work.

9.  Informative References

   [I-D.ietf-cats-usecases-requirements]
              Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
              "Computing-Aware Traffic Steering (CATS) Problem
              Statement, Use Cases, and Requirements", Work in Progress,
              Internet-Draft, draft-ietf-cats-usecases-requirements-10,
              30 November 2025, <https://datatracker.ietf.org/doc/html/
              draft-ietf-cats-usecases-requirements-10>.

   [I-D.cx-green-ps]
              Clemm, A., Westphal, C., Tantsura, J., Ciavaglia, L., and
              M. Odini, "Challenges and Opportunities in Management for
              Green Networking", Work in Progress, Internet-Draft,
              draft-cx-green-ps-02, 13 March 2023,
              <https://datatracker.ietf.org/doc/html/draft-cx-green-ps-
              02>.

   [I-D.cx-opsawg-green-metrics]
              Clemm, A., Dong, L., Mirsky, G., Ciavaglia, L., Tantsura,
              J., Odini, M., Schooler, E., Rezaki, A., and C. Pignataro,
              "Green Networking Metrics", Work in Progress, Internet-
              Draft, draft-cx-opsawg-green-metrics-02, 4 March 2024,
              <https://datatracker.ietf.org/doc/html/draft-cx-opsawg-
              green-metrics-02>.

Authors' Addresses

   Jing Wang
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: wangjingjc@chinamobile.com

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   Yuexia Fu
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: fuyuexia@chinamobile.com

   Cheng Li
   Huawei Technologies
   Huawei Campus, No. 156 Beiqing Rd.
   Beijing
   100095
   China
   Email: c.l@huawei.com

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