AI/ML-Enabled Computing Aware Traffic Steering using IP address anchoring
draft-bernardos-cats-anchoring-aiml-selection-01
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Carlos J. Bernardos , Alain Mourad | ||
| Last updated | 2026-09-17 | ||
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| Intended RFC status | (None) | ||
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draft-bernardos-cats-anchoring-aiml-selection-01
CATS WG CJ. Bernardos
Internet-Draft UC3M
Intended status: Standards Track A. Mourad
Expires: 21 March 2027 InterDigital
17 September 2026
AI/ML-Enabled Computing Aware Traffic Steering using IP address
anchoring
draft-bernardos-cats-anchoring-aiml-selection-01
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.
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 21 March 2027.
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 or 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 are 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 entity 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
instantiating 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 a 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 document 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 respond 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 request to selected service anchor: |
| | CATS request | | | | | | | |
| |------------------------>| | | | | | |
| |6. ECR sends an 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-01, 16 April 2026,
<https://datatracker.ietf.org/doc/html/draft-bernardos-
cats-anchoring-site-mobility-01>.
[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-24, 2 April 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-cats-
framework-24>.
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/
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