



CATS WG                                                    CJ. Bernardos
Internet-Draft                                                      UC3M
Intended status: Standards Track                               A. Mourad
Expires: 2 September 2026                                   InterDigital
                                                            1 March 2026


    AI/ML-Enabled Computing Aware Traffic Steering using IP address
                               anchoring
            draft-bernardos-cats-anchoring-aiml-selection-00

Abstract

   The IETF CATS WG addresses the problem of how the network
   infrastructure can steer traffic between clients of a service and
   sites offering the service, considering both network metrics (such as
   bandwidth and latency), and compute metrics (such as processing,
   storage capabilities, and capacity).

   This document describes solutions to enable the network to select the
   best site to instantiate a processing service (using distributed
   sensing as an application example), augmenting CATS enabled solutions
   that consider both connectivity and computing, to also consider AI/ML
   and data capabilities and governance policies.

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
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   This Internet-Draft will expire on 2 September 2026.

Copyright Notice

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





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   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
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction and Problem Statement  . . . . . . . . . . . . .   2
     1.1.  Use case scenario . . . . . . . . . . . . . . . . . . . .   2
     1.2.  Problem statement . . . . . . . . . . . . . . . . . . . .   5
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   3.  Enabling AI/ML-aware CATS with IP address anchoring . . . . .   6
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   6.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  13
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction and Problem Statement

1.1.  Use case scenario

   There are sensing scenarios and use cases that involve a distributed
   sensing task, in which one ore multiple sensors participate, and that
   requires a supporting sensing service (e.g., fusing sensing
   measurements from different sensors, and/or applying AI/ML techniques
   to process and obtain an accurate sensing result).  This sensing
   service needs to be executed on some sort of sensing processing/
   computing function that would typically require AI/ML capabilities to
   provide accurate results.  Being capable of processing the results at
   a node that has been trained with the proper data and/or has the
   required computing capabilities for the AI/ML processing is key for
   the accuracy and timeliness of the sensing results.  That adds one
   more requirement, in addition to the connectivity and computing ones,
   to be able to properly operate (and deliver timely the sensing
   results).

   A terminal requests a sensing service with certain i) connectivity
   and computing associated requirements (CATS requirements), ii) AI/ML
   processing requirements, and potentially iii) sensing data governance
   requirements about privacy, security and trustworthiness.  Multiple
   sites where the service can be instantiated exist in the domain where
   the terminal is attached.  The network selects the best site to
   instantiate the service, instantiates it, and provides an IP address



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   to the terminal.  This address is anchored at a router close (or at)
   the site where the selected service instance runs.  Computing,
   connectivity, AI/ML and data governance related demands are met.

   Note that this is just an example, other services would also benefit
   from compute and connectivity traffic steering.  For the sake of
   having a simpler service, we can also consider an AR/VR/XR service
   where a terminal connected to the network needs to instantiate a
   service in the network to aid in the AR/VR/XR service by providing
   computing capabilities with latency constraints.

   Note on terminology.  In this document we use the old terminology in
   which by ICR we mean Ingress CATS-Forwarder
   [I-D.ietf-cats-framework], and by ECR we mean Egress CATS-Forwarder.

   Figure 1 shows an exemplary scenario.  There is a distributed sensing
   task (e.g., requested by an Application Function or Network
   Function).  This involves one sensor function (hosted at terminal #1)
   and a sensing processing function (which can be potentially hosted at
   several service sites).  In general, the sensor(s) might be of the
   same or different technology and might be connected to the same RAN
   or different ones (which might also be of different access
   technologies).  The selection of the composition of the sensing group
   is out of the scope of this document.

   In this particular example, the processing of the sensing data poses
   some specific requirements, not only in terms of general-purpose
   computing and connectivity (what we generally refer to as "CATS
   requirements"), but also in terms of AI/ML processing, data training
   capabilities (e.g., it is preferred to process the data at a site
   that has already a model trained fitting this specific scenario) and
   data policy/governance requirements.  In this example, service site
   #n-1 is the one selected.


















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                               AI/ML and data
                               capabilities Y
                              ________________
                             (     ---------- )
  AI/ML and data            (      |        |  )       AI/ML and data
  capabilities X           (     ---------- |   )      capabilities Z
      ________________     (     |        | |    )     _______________
     (     ---------- )    (   ---------- | |    )    (    ---------- )
    (      |        |  )   (   |service | |-     )   (     |        |  )
   (     ---------- |   )  (   |contact | |      )  (    ---------- |  )
   (     |        | |   )  (   |instance|--      )  (    |        | |  )
   (   ---------- | |   )   (  ----------       )   (  ---------- | |  )
   (   |service | |-    )    ( Serv. site #N-1 )   (   |service | |-   )
   (   |contact | |     )     -------+----------    (  |contact | |   )
   (   |instance|--    )   Computing  \             (  |instance|--   )
    (  ----------     )    delay:4ms   \             ( ----------     )
     ( Serv. site #1 )           --------+--          ( Serv. site #N )
      -------+--------       ----| ECR#N-1 |----       ---------+-----
              \  Computing --     -----------    -- Computing  /
               \ delay:10ms      Networking         delay:5ms /
             ---+-----           delay:7ms             ------+--
           ( | ECR#1 |            //                   | ECR#N | )
          (  ---------           //                    ---------  )
         ( Networking           //                      Networking )
        (  delay:5ms           //                        delay:15ms )
       (                      //                                     )
       (                     //                                      )
        (                   //                                      )
         (                 //                                      )
          (               //                                      )
           (       ---------                     ---------       )
            -------| AN#1  |                     | AN#2  |      )
                   | ICR#1 |---------------------| ICR#2 |------
                   ---------        ______       ---------
                   (o)             (      )
                  (o)             ( object )
                 (o)             / (______)
                (o)             /
               (o)             /  (sensing)
              --------------  /
              | terminal#1 | /
              --------------

                     Figure 1: Exemplary scenario







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1.2.  Problem statement

   The main problem that this document tries to address is the
   following: current networking systems mainly take into consideration
   connectivity characteristics when deciding how to route traffic.
   While some recent mechanisms start to consider jointly compute and
   networking, there are no solutions that account at the same time for
   AI/ML and trained models' availability and data governance policies.

   Based on the former, this document proposes solutions to enable the
   network to select the best site to instantiate a sensing processing
   service, augmenting CATS enabled solutions that consider both
   connectivity and computing, to also consider AI/ML and data
   capabilities and governance policies.  In particular, this document
   addresses the following question: what information does the network
   need to select most suitable AI/ML-enabled sensing service to be
   instantiated?, leveraging the architecture defined in
   [I-D.bernardos-cats-ip-address-anchoring]?

2.  Terminology

   The following terms used in this document are defined by the IETF:

      ECR: Egress CATS router.  This refers to the Egress CATS-Forwarder
      as defined in [I-D.ietf-cats-framework].

      ICR: Ingress CATS router.  This refers to the Ingress CATS-
      Forwarder as defined in [I-D.ietf-cats-framework].

   The following terms ara used in this document:

      (Distributed) Sensing Group: a group of devices participating on a
      sensing task.

      Sensing Traffic: traffic used (after some processing) to generate
      a sensing result.

      Sensing Processing Function: a function processing sensing traffic
      (potentially from different sources) to generate a sensing result
      (or something that can be further processed to generate a sensing
      result).

      Sensing Signal: radio signal used in the processing.

      Sensor Function: function running on a device participating on a
      sensing task that generates and/or processes a sensing signal.





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      ISF: integrated sensing function.  This is a logical in charge of
      controlling the distributed sensing task.

      CATS Agent: logical entity performing a function related to
      computing aware traffic steering.

   Note that we use UE or terminal to refer to a mobile host.

3.  Enabling AI/ML-aware CATS with IP address anchoring

   We describe next an example of operation and signaling for the
   network to be able to select the best site to instantiate a sensing
   service consumed by a terminal, so traffic can be steered, not only
   simultaneously meeting connectivity and computing requirements, but
   also considering AI/ML capabilities and availability of suited data/
   trained models and data governance policies.  A CATS agent runs on
   both the ingress (the router to which the terminal is attached to)
   and egress (a router close to or at the site where the service
   instance is running) routers, and also at the sites capable of
   instating services.  A CATS agent functionality can also run on the
   terminal to aid the network deciding or actively influence its site
   selection.  A CATS agent might also run on logical controller entity
   which might be hosted at the network infrastructure.  In addition to
   the functionality defined in
   [I-D.bernardos-cats-ip-address-anchoring],
   [I-D.bernardos-cats-anchoring-service-mobility] and
   [I-D.bernardos-cats-anchoring-site-mobility], this documents defines
   a new functionality:

   *  AI/ML and data/models: it deals with the procedures required to
      (i) monitor and track capabilities relevant for AI/ML processing,
      including hardware enablers, trained models, data available for
      training and/or participating in a distributed learning, data
      governance policies, etc; (ii) perform the required signaling to
      facilitate distributed learning mechanisms.

   Figure 2 shows the message sequence chart of the AI/ML-aware IP
   address service-specific anchoring for CATS which is explained next:













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 +----+ +-----+ +-------------+ +---------------+ +-------------+ +----+
 |    | |     | |   site #1   | |   site #N-1   | |   site #N   | |CATS|
 |term| |ICR#1| |ECR#1 ag. SCI| |ECR#N-1 ag. SCI| |ECR#N ag. SCI| |ctrl|
 +----+ +-----+ +--+----+---+-+ +----+----+---+-+ +--+----+---+-+ +----+
    |      |       |    |   |        |    |   |      |    |   |      |
 1. Service req.   |    |   |        |    |   |      |    |   |      |
    |----->|       |    |   |        |    |   |      |    |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
    |  OPTION 1:   |    |   |        |    |   |      |    |   |      |
    |  2a. ICR queries candidate service anchors:    |    |   |      |
    |      | CATS query |   |        |    |   |      |    |   |      |
    |      |------>|<-->|   |        |    |   |      |    |   |      |
    |      |------------------------>|<-->|   |      |    |   |      |
    |      |---------------------------------------->|<-->|   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
    |      2b. ECRs respod based on site conditions: |    |   |      |
    |      |       |    |   |        | CATS response |    |   |      |
    |      |<------|    |   |        |    |   |      |    |   |      |
    |      |<------------------------|    |   |      |    |   |      |
    |      |<----------------------------------------|    |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
    |  OPTION 2:   |    |   |        |    |   |      |    |   |      |
    |  3a. ICR queries CATS controller:   |   |      |    |   |      |
    |      | CATS query |   |        |    |   |      |    |   |      |
    |      |-------------------------------------------------------->|
    |      |       |    |   |        | 3b. CATS controller responds: |
    |      |       |    |   |        |    |   |      | CATS response |
    |      |<--------------------------------------------------------|
    |      |       |    |   |        |    |   |      |    |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
  (4. Service anchor/Egress CATS router @ site n-q is selected as best)
    |      |5. ICR sends a CAT rquest to selected service anchor:    |
    |      | CATS request   |        |    |   |      |    |   |      |
    |      |------------------------>|    |   |      |    |   |      |
    |      |6. ECR sends and ACK:    |    |   |      |    |   |      |
    |      |       |    |   CATS ACK |    |   |      |    |   |      |
    |      |<------------------------|    |   |      |    |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
    |      |       |     (7. A tunnel is established)     |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |
 8. Assigned IP prefix/addr., service specific policy|    |   |      |
    |<-----|       |    |   |        |    |   |      |    |   |      |
    |     (9. Service specific traffic)   |   |      |    |   |      |
    |<---->|<----------------------->|<------>|      |    |   |      |
    |      |       |    |   |        |    |   |      |    |   |      |

                     Figure 2: Exemplary signaling




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   0.   A terminal wants to execute a sensing service on a capable node
        in the network infrastructure.  This service has not only
        connectivity and computing requirements, but also requires some
        AI/ML processing to provide accurate and timely results.  We
        refer to the connectivity and computing requirements as CATS
        requirements, and to the AI/ML processing and trained models/
        data availability and associated data governance policies as AI/
        ML requirements.

   1.   The terminal sends a Sensing service request to the ICR,
        including a service ID and, optionally, if the terminal is CATS
        aware, a list of CATS requirements and AI/ML requirements.  Note
        that this request might be addressed to an ICR or just
        intercepted by an ICR.  If present, the list of AI/ML
        requirements might include information such as (not limited to
        any particular combination of parameters):

        *  Processing related requirements, such as, but not limited to:

           i.    Target hardware required resources (e.g., GPUs).

           ii.   Target AI/ML processing with provided data.

           iii.  Size of the data to be processed.

           iv.   Does the sensing processing function need to be trusted
                 or not by the network?

           v.    Requirements on available specific trained models to be
                 used.

           vi.   Target on AI explainability for the used models.

           vii.  Requirements for available data for potential
                 distributed training on demand.

        *  Data governance related requirements, such as, but not
           limited to:

           i.    Security of the data (how and where the data can be
                 stored and isolated, etc.).

           ii.   Privacy of the data (which entities may gain access to
                 the data and in what form e.g. partial, full,
                 anonymized).






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           iii.  Trustworthiness of the data (how to make use of the
                 data and which entities may be using it and for what
                 purpose).

           iv.   Quality of the data (e.g., the quality of the data used
                 to train the model used for the processing).

        Additionally, and optionally, the terminal might also include
        some sensing context information that can help decide what AI/ML
        processing to use, or be used, as a parameter to the model, such
        as:

        *  Specification and/or characteristics of the radio hardware
           used for sensing (e.g., chipset, antennae, configuration,
           etc.).  This might be used to select the most appropriate
           model (for example if there is one trained already for this
           hardware configuration).

        *  Monitored characteristics of the wireless media used for
           sensing (such as received power, interference, etc.).  This
           might be used to select the most appropriate model (for
           example if there is one trained already for these specific
           conditions).

        *  Information about physical environment, such as presence of
           reference objects, that can be used as an additional
           parameter of the sensing processing function to provide more
           accurate results.  Another example would be positioning
           coordinates.

        *  Metadata characterizing desirable data for potential
           distributed training of a model to be used for the processing
           of the sensing data.

        There are two main options considered:

   2.   OPTION 1:

        a.  The ICR sends a query to all ECRs of the domain, or a subset
            selected based on the location of the ICR.  This query may
            include the following parameters:

            i.    Service ID: an identifier of the service requested by
                  the terminal.  This allows to check if the service can
                  be instantiated, or it is already instantiated.






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            ii.   Terminal ID: an identifier of the terminal requesting
                  the service.  This is useful for example for affinity
                  purposes.  It might not include information that can
                  be used to identify the user.

            iii.  ICR ID: identifier of the requesting ICR.

            iv.   CATS requirements: list of requirements, e.g.,
                  connectivity and computing requirements.

            v.    AI/ML requirements: list of requirements to be useful
                  to filter and rank potential instances.

            vi.   Sensing context: additional data that is useful in the
                  instance selection, the AI/ML model and configuration
                  as additional parameters for the AI/ML training and/or
                  inference.

        b.  Each ECR, possibly after checking with the CATS agent of the
            site(s) it provides connectivity, responds, including the
            following information:

            i.    Service ID.

            ii.   Terminal ID.

            iii.  ECR ID: identifier of the ECR sending the response.

            iv.   CATS conditions: how the site meets each of the
                  requirements included in the request.

            v.    AI/ML capabilities: describing how the site meets each
                  of the requirements included in the request, and
                  information about available data and trained models
                  available at the site.

            vi.   (Optional): URI to get to the service instance.  A
                  CATS agent at a site might be collocated with the ECR.
                  Examples of a CATS agent at a site are network
                  controllers or orchestrators at the site.  Note that
                  the way a CATS agent at an ECR may interact with the
                  CATS agent of the site is out of the scope of this
                  document.  Examples include using monitoring and
                  telemetry interfaces with an orchestrator managing the
                  site.

            Based on the received responses, the ICR selects an ECR.
            (step 4).



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   3.   OPTION 2:

        a.  The ICR sends a query to a CATS controller in the domain,
            including the following parameters:

            i.    Service ID: an identifier of the service requested by
                  the terminal.  This allows to check if the service can
                  be instantiated, or it is already instantiated.

            ii.   Terminal ID: an identifier of the terminal requesting
                  the service.  This is useful for example for affinity
                  purposes.  It might not include information that can
                  be used to identify the user.

            iii.  ICR ID: identifier of the requesting ICR.

            iv.   CATS requirements: list of requirements, e.g.,
                  connectivity and computing requirements.

            v.    AI/ML requirements: list of requirements to be useful
                  to filter and rank potential instances.

            vi.   Sensing context: additional data that is useful in the
                  instance selection, the AI/ML model and configuration
                  as additional parameters for the AI/ML training and/or
                  inference.

        b.  The CATS controller, which has the overall view of all the
            sites and ECRs of the domain, responds back including the
            following information:

            i.    Service ID.

            ii.   Terminal ID.

            iii.  ECR ID: identifier of the ECR sending the response.

            iv.   CATS conditions: how the site meets each of the
                  requirements included in the request.

            v.    Selected ECR: IP address of the selected ECR.

            vi.   AI/ML capabilities: describing how the site meets each
                  of the requirements included in the request, and
                  information about available data and trained models
                  available at the site.





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   4.   At this point, there is an ECR (and site) selected for use for
        the specific service requested by the terminal.

   5.   The ICR requests the proposed/selected ECR to establish a
        traffic steering session with it, sending a CATS request.  This
        request includes the same information that was included in the
        CATS query (to facilitate stateless operation of the ECRs while
        being queried).

   6.   The selected ECR, if it accepts the request, responds back with
        an acknowledgement, including the following information:

        *  Service ID.

        *  Terminal ID.

        *  ECR ID: identifier of the ECR sending the response.

        *  CATS conditions: how the site meets each of the requirements
           included in the request.

        *  AI/ML capabilities: describing how the site meets each of the
           requirements included in the request, and information about
           available data and trained models available at the site.

        *  IP prefix assigned for the terminal to use to reach the
           service instance.

        *  (Optional): URI to get to the service instance.

   7.   An IP tunnel is established between the ICR and the selected
        ECR.  Forwarding is also setup so traffic going from/to the
        allocated IP prefix is sent through the tunnel at the ICR/ECR.

   8.   The ICR conveys the allocated IP prefix to the terminal.  This
        can be done using Router Advertisements, optionally enhanced
        with RFC 4191 policies for the selected service.  Alternatively,
        other options such as DHCP can be used to provide the prefix.

   9.   Traffic of the service for this terminal is steered using the IP
        tunnel.

4.  IANA Considerations

   TBD.






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5.  Security Considerations

   TBD.

6.  Acknowledgments

   The work of Carlos J.  Bernardos in this document has been partially
   supported by the Horizon Europe MultiX (Grant 101192521), DISCO6G-CM
   (TEC-2024/COM-360) and UNICO I+D 6G-DATADRIVEN projects.

7.  Informative References

   [I-D.bernardos-cats-anchoring-service-mobility]
              Bernardos, C. J. and A. Mourad, "Service Mobility-Enabled
              Computing Aware Traffic Steering using IP address
              anchoring", Work in Progress, Internet-Draft, draft-
              bernardos-cats-anchoring-service-mobility-04, 24 September
              2025, <https://datatracker.ietf.org/doc/html/draft-
              bernardos-cats-anchoring-service-mobility-04>.

   [I-D.bernardos-cats-anchoring-site-mobility]
              Bernardos, C. J. and A. Mourad, "Site Mobility-Enabled
              Computing Aware Traffic Steering using IP address
              anchoring", Work in Progress, Internet-Draft, draft-
              bernardos-cats-anchoring-site-mobility-00, 19 October
              2025, <https://datatracker.ietf.org/doc/html/draft-
              bernardos-cats-anchoring-site-mobility-00>.

   [I-D.bernardos-cats-ip-address-anchoring]
              Bernardos, C. J. and A. Mourad, "Computing Aware Traffic
              Steering using IP address anchoring", Work in Progress,
              Internet-Draft, draft-bernardos-cats-ip-address-anchoring-
              04, 24 September 2025,
              <https://datatracker.ietf.org/doc/html/draft-bernardos-
              cats-ip-address-anchoring-04>.

   [I-D.ietf-cats-framework]
              Li, C., Du, Z., Boucadair, M., Contreras, L. M., and J.
              Drake, "A Framework for Computing-Aware Traffic Steering
              (CATS)", Work in Progress, Internet-Draft, draft-ietf-
              cats-framework-20, 26 February 2026,
              <https://datatracker.ietf.org/doc/html/draft-ietf-cats-
              framework-20>.

Authors' Addresses






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   Carlos J. Bernardos
   Universidad Carlos III de Madrid
   Av. Universidad, 30
   28911 Leganes, Madrid
   Spain
   Phone: +34 91624 6236
   Email: cjbc@it.uc3m.es
   URI:   http://www.it.uc3m.es/cjbc/


   Alain Mourad
   InterDigital Europe
   Email: Alain.Mourad@InterDigital.com
   URI:   http://www.InterDigital.com/





































Bernardos & Mourad      Expires 2 September 2026               [Page 14]
