Computing-Aware Traffic Steering (CATS) Problem Statement, Use Cases, and Requirements
draft-ietf-cats-usecases-requirements-12
| Document | Type | Active Internet-Draft (cats WG) | |
|---|---|---|---|
| Authors | Kehan Yao , Luis M. Contreras , Hang Shi , Shuai Zhang , Qing An | ||
| Last updated | 2026-01-16 (Latest revision 2026-01-12) | ||
| Replaces | draft-yao-cats-ps-usecases | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | Informational | ||
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| Stream | WG state | Submitted to IESG for Publication | |
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| Document shepherd | Daniel Huang | ||
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draft-ietf-cats-usecases-requirements-12
cats K. Yao
Internet-Draft China Mobile
Intended status: Informational L. M. Contreras
Expires: 17 July 2026 Telefonica
H. Shi
Huawei Technologies
S. Zhang
China Unicom
Q. An
Alibaba Group
13 January 2026
Computing-Aware Traffic Steering (CATS) Problem Statement, Use Cases,
and Requirements
draft-ietf-cats-usecases-requirements-12
Abstract
Distributed computing is a computing pattern that service providers
can follow and use to achieve better service response time and
optimized energy consumption. In such a distributed computing
environment, compute intensive and delay sensitive services can be
improved by utilizing computing resources hosted in various computing
facilities. Ideally, compute services are balanced across servers
and network resources to enable higher throughput and lower response
time. To achieve this, the choice of server and network resources
should consider metrics that are oriented towards compute
capabilities and resources instead of simply dispatching the service
requests in a static way or optimizing solely on connectivity
metrics. The process of selecting servers or service instance
locations, and of directing traffic to them on chosen network
resources is called "Computing-Aware Traffic Steering" (CATS).
This document provides the problem statement and the typical
scenarios for CATS, which shows the necessity of considering more
factors when steering traffic to the appropriate computing resource
to better meet the customer's expectations.
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/.
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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 17 July 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 . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 4
3. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Multi-deployment of Edge Service Sites and Service . . . 4
3.2. Traffic Steering among Edges Service Sites and Service
Instances . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.1. Example 1: Computing-aware AR or VR . . . . . . . . . . . 9
4.2. Example 2: Computing-aware Intelligent Transportation . . 12
4.3. Example 3: Computing-aware Digital Twin . . . . . . . . . 13
4.4. Example 4: Computing-aware SD-WAN . . . . . . . . . . . . 14
4.5. Example 5: Computing-aware Distributed AI Training and
Inference . . . . . . . . . . . . . . . . . . . . . . . . 15
4.5.1. Distributed AI Inference . . . . . . . . . . . . . . 16
4.5.2. Distributed AI Training . . . . . . . . . . . . . . . 17
4.6. Discussion . . . . . . . . . . . . . . . . . . . . . . . 18
5. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 18
5.1. Support Dynamic and Effective Selection among Multiple
Service Instances . . . . . . . . . . . . . . . . . . . . 19
5.2. Support Agreement on Metric Representation and
Definition . . . . . . . . . . . . . . . . . . . . . . . 19
5.3. Use of CATS Metrics . . . . . . . . . . . . . . . . . . . 21
5.4. Support Instance Affinity . . . . . . . . . . . . . . . . 22
5.5. Preserve Communication Confidentiality . . . . . . . . . 24
5.6. Correlation between Use Cases and Requirements . . . . . 24
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6. Security Considerations . . . . . . . . . . . . . . . . . . . 26
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 27
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 27
8.1. Normative References . . . . . . . . . . . . . . . . . . 27
8.2. Informative References . . . . . . . . . . . . . . . . . 27
Appendix A. Appendix A . . . . . . . . . . . . . . . . . . . . . 28
A.1. Integrated Sensing and Communications (ISAC) . . . . . . 29
A.1.1. Requirements . . . . . . . . . . . . . . . . . . . . 31
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 31
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 33
1. Introduction
Network operators are increasingly deploying computing resources,
with a focus on enhancing edge capabilities to support services
requiring low latency, high reliability, and dynamic resource
scaling.
Diversified service demands have brought key challenges to service
deployment and traffic scheduling. A single-site service instance
often lacks sufficient capacity to guarantee the required quality of
service, especially during peak hours when local computing resources
may fail to handle all incoming requests, leading to longer response
times or even request drops. Regular capacity expansion of a single
site is neither practical nor economical. Additionally, relying
solely on end-side computing enhancements cannot meet the computing
requirements of all applications.
It is necessary to deploy services across multiple sites (either edge
or central nodes) to improve availability and scalability. To this
end, traffic should be steered to the "best" service instance based
on factors like current computing load, where "best" is largely
determined by application requirements.
However, existing routing schemes and traffic engineering methods
fall short of addressing these challenges. The underlying networking
infrastructures that include computing resources usually provide
relatively static service dispatching or depend solely on
connectivity metrics for traffic steering, failing to account for
compute capabilities and resource status, which are critical for
meeting the quality requirements of modern services.
To tackle this issue, the choice of service instance and network
resources should further consider compute-oriented metrics beyond
connectivity metrics. The process of selecting service instances and
locations based on metrics that are oriented towards compute
capabilities and resources, and of directing traffic to them on
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chosen network resources is called "Computing-Aware Traffic Steering"
(CATS). It should be noted that CATS is not limited to edge
computing scenarios, however, Section 3 of this document will focus
on edge computing scenarios for problem statement.
This document describes sample usage scenarios that drive CATS
requirements and will help to identify candidate solution
architectures and approaches.
2. Definition of Terms
This document uses the terms defined in [I-D.ietf-cats-framework],
including service site, service instance, CATS service identifier(CS-
ID).
Edge Computing: Edge computing is a computing pattern that moves
computing infrastructures, i.e, servers, away from centralized data
centers and instead places it close to the end users for low
latency communication.
Even though this document is not a protocol specification, it makes
use of upper case key words to define requirements unambiguously.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Problem Statement
3.1. Multi-deployment of Edge Service Sites and Service
In edge computing environments, service instances typically adopt a
multi-site deployment model. It should be clarified that specific
service instance deployment strategies are not within the scope of
CATS. However, it is undeniable that there is a close correlation
between service instance deployment and traffic scheduling,
especially in the definition and selection of core metrics such as
computing capabilities and resources. This dual applicability allows
a common set of metrics to inform both traffic steering and higher-
level service management decisions, without requiring CATS to define
orchestration behavior.
Therefore, to present a clear and comprehensive problem statement, it
is necessary to first introduce the relevant considerations for
multi-edge service site deployment. This premise can better support
the subsequent elaboration on CATS requirements and solutions.
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The core goal of edge computing is to provide computing services
closer to users through shorter network paths. Before deploying edge
service sites, the following factors need to be considered:
* Geographic location: Including the number of users, differences in
service types, and the number of connection requests from users.
For edge service sites located in densely populated areas with a
large number of users and service requests, more service replicas
can be deployed compared to other areas.
* The type, scale, and usage frequency of required computing
resources. For example, distributed AI inference services require
the deployment of more GPU resources.
* The status of network resources associated with computing
resources, such as network topology, network access methods,
connectivity, link bandwidth, and path protection or redundancy
information.
To improve the overall quality of service, during the service
deployment phase, it is necessary to analyze the approximate network
and computing resource requirements of the service, comprehensively
form a reasonable network and computing resource topology, and
clarify the location, overall distribution, and relative position of
computing resources in the network topology. This process relies on
standardized consensus on computing and network resources related
metrics, which is also the point most closely related to the problem
space addressed by CATS traffic scheduling.
3.2. Traffic Steering among Edges Service Sites and Service Instances
This section describes how existing edge computing systems do not
provide all of the support needed for real-time or near-real-time
services, and how it is necessary to steer traffic to different sites
considering changes in client distribution, different time slots,
events, server loads, network capabilities, and some other factors
which might not be directly measured, i.e., properties of edge
service sites(e.g., geographical location), etc.
It's assumed that service instances are multi-site deployed, and they
are reachable through a network infrastructure.
When a client issues a service request for a required service, the
request is steered to one of the available service instances. Each
service instance may act as a client towards another service, thereby
seeing its own outbound traffic steered to a suitable service
instance of the requested service and so on, achieving service
composition and chaining as a result.
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The aforementioned selection of a service instance from the set of
candidates is performed using traffic steering methods.
In edge computing, traffic is steered to an edge service site that is
"closest" or to one of a few "close" sites using load-balancing.
Such traffic steering can be initiated either by the application
layer or by the network layer: the application layer may actively
query for the optimal node and guide traffic using mechanisms such as
the ALTO protocol[RFC7285], while the network layer may leverage
Anycast routing[RFC4786], where routering systems automatically
distribute traffic according to routing tables in an application-
transparent manner. However, regardless of whether the steering is
performed by the application or the network, the core criteria for
selecting "closest" or "close" sites often rely solely on
communication metrics (such as physical distance, hop count, or
network latency). This decision logic can easily lead to suboptimal
choices, meaning that the "closest" site is not always the "best"
one. This is because the computing resources and states of edge
service sites can change in real time:
* The closest site may not have sufficient resources.
* The closest site may not have the specific computing resources
required.
To address these issues, enhancements to traffic steering mechanisms
are needed to direct traffic to sites that can adequately support the
requested services. Steering decision may take into account more
complex and possibly dynamic metric information, such as load of
service instances, latency experienced or similar, for selection of a
more suitable service instance.
It is important to note that clients may move. This means that the
service instance that was "best" at one moment might no longer be
best when a new service request is issued. This creates a (physical)
dynamicity that will need to be catered for in addition to the
changes in server and network load. From a routing perspective, CATS
is an application-transparent routing mechanism that can provide
scheduling for both stateful and stateless services. However, in
scenarios where clients move and the service is stateful, CATS
requires the application to explicitly indicate whether it allows the
routing system to enable CATS functionality. Otherwise, mid-session
scheduling triggered by CATS may cause application context
inconsistency among service sites or even service interruption.
Figure 1 shows a common way to deploy edge service sites in the
metro. Edge service sites are connected with Provider Edges(PEs).
There is an edge data center for metro area which has high computing
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resource and provides the service to more User Equipments(UEs) (UE1
to UEn) at the working time. Because more office buildings are in
the metro area. And there are also some remote edge service sites
which have limited computing resource and provide the service to the
UEs (UEa, UEb) close to them.
Applications to meet service demands could be deployed in both the
edge data center in metro area and the remote edge service sites. In
this case, the service request and the resource are matched well.
Some potential traffic steering may be needed just for special
service request or some small scheduling demand.
+----------------+ +---+ +------------+
+----------------+ |- - |UE1| +------------+ |
| +-----------+ | | +---+ +--| Edge | |
| |Edge server| | | +---+ +- - -|PE| | |
| +-----------+ | |- - |UE2| | +--| Site 1 |-+
| +-----------+ | | +---+ +------------+
| |Edge server| | | ... | |
| +-----------+ | +--+ Potential +---+ +---+
| +-----------+ | |PE|- - - - - - -+ |UEa| |UEb|
| |Edge server| | +--+ Steering +---+ +---+
| +-----------+ | | +---+ | |
| +-----------+ | |- - |UE3| +------------+
| | ... ... | | | +---+ | +------------+ |
| +-----------+ | | ... +--| Edge | |
| | | +---+ +- - -|PE| | |
|Edge data center|-+- - |UEn| +--| Site 2 |-+
+----------------+ +---+ +------------+
High computing resource Limited computing resource
and more UE at metro area and less UE at remote area
Figure 1: Common Deployment of Edge Service Sites
Figure 2 shows that during non-working hours, for example at weekend
or daily night, more UEs move to the remote area that are close to
their house or for some weekend events. So there will be more
service request at remote but with limited computing resource, while
the rich computing resource might not be used with less UE in the
metro area. It is possible for many people to request services at
the remote area, but with the limited computing resource, moreover,
as the people move from the metro area to the remote area, the edge
service sites that serve common services will also change, so it may
be necessary to steer some traffic back to the metro data center.
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+----------------+ +------------+
+----------------+ | +------------+ |
| +-----------+ | | Steering traffic +--| Edge | |
| |Edge server| | | +-----------|PE| | |
| +-----------+ | | | +--| Site 1 |-+
| +-----------+ | |- - - - - - - -+ +-+----------+
| |Edge server| | | | | | |
| +-----------+ | +--+ | +---+ +---+ +---+ +---+ +---+
| +-----------+ | |PE|-------+ |UEa| |UEb| |UE1| |...| |UEn|
| |Edge server| | +--+ | +---+ +---+ +---+ +---+ +---+
| +-----------+ | | | | |
| +-----------+ | |- - - - - - - - - - -+ +------+
| | ... ... | | | | +------------+ |
| +-----------+ | | | +--| Edge | |
| | | +-----------|PE| | |
|Edge data center|-+ Steering traffic +--| Site 2 |-+
+----------------+ +------------+
High computing resource Limited computing resource
and less UE at metro area and more UE at remote area
Figure 2: Steering Traffic among Edge Service Sites
There will also be the common variable of network and computing
resources, for someone who is not moving but get a poor latency
sometime. Because of other UEs moving, a large number of request for
temporary events such as vocal concert, shopping festival and so on,
and there will also be the normal change of the network and computing
resource status. So for some fixed UEs, it is also expected to steer
the traffic to appropriate sites dynamically.
Those problems indicate that traffic needs to be steered among
different edge service sites, because of the mobility of the UE and
the common variable of network and computing resources. Moreover,
some use cases in the following section require both low latency and
high computing resource usage or specific computing hardware
capabilities (such as local GPU); hence joint optimization of network
and computing resource is needed to guarantee the Quality of
Experience (QoE).
4. Use Cases
This section presents a non-exhaustive set of use cases which would
benefit from the dynamic selection of service instances and the
steering of traffic to those service instances.
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4.1. Example 1: Computing-aware AR or VR
Cloud VR/AR introduces the concept of cloud computing to the
rendering of audiovisual assets in such applications. Here, the edge
cloud helps encode/decode and render content. The end device usually
only uploads posture or control information to the edge and then VR/
AR contents are rendered in the edge cloud. The video and audio
outputs generated from the edge cloud are encoded, compressed, and
transmitted back to the end device or further transmitted to central
data center via high bandwidth networks.
A Cloud VR service is delay-sensitive and influenced by both network
and computing resources. Therefore, the edge service site which
executes the service has to be carefully selected to make sure it has
sufficient computing resource and good network condition to guarantee
the end-to-end service delay. For example, for an entry-level cloud
VR (panoramic 8K 2D video) with 110-degree Field of View (FOV)
transmission, the typical network requirements are bandwidth 40Mbps,
20ms for motion-to-photon latency, packet loss rate is 2.4E-5; the
typical computing requirements are 8K H.265 real-time decoding, 2K
H.264 real-time encoding. Further, the 20ms latency can be
categoried as:
(i) Sensor sampling delay(client), which is considered
imperceptible by users is less than 1.5ms including an extra
0.5ms for digitalization and end device processing.
(ii) Display refresh delay(client), which take 7.9ms based on the
144Hz display refreshing rate and 1ms extra delay to light up.
(iii) Image/frame rendering delay(server), which could be reduced to
5.5ms.
(iv) Round-trip network delay: The remaining latency budget is 5.1
ms, calculated as 20-1.5-5.5-7.9 = 5.1ms.
So the budgets for server(computing) delay and network delay are
almost equivalent, which make sense to consider both of the delay for
computing and network. And it could't meet the total delay
requirements or find the best choice by either optimizing the network
or computing resource.
Based on the analysis, here are some further assumption as Figure 3
shows, the client could request any service instance among 3 edge
service sites. The delay of client could be same, and the
differences of edge service sites and corresponding network path have
different delays:
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* Edge service site 1: The computing delay=4ms based on a light
load, and the corresponding network delay=9ms based on a heavy
traffic.
* Edge service site 2: The computing delay=10ms based on a heavy
load, and the corresponding network delay=4ms based on a light
traffic.
* Edge service site 3: The edge service site 3's computing delay=5ms
based on a normal load, and the corresponding network delay=5ms
based on a normal traffic.
In this case, we can't get an optimal network and computing total
delay if choosing the resource only based on either of computing or
network status:
* The edge service site based on the best computing delay it will be
the edge service site 1, the E2E delay=22.4ms.
* The edge service site based on the best network delay it will be
the edge service site 2, the E2E delay=23.4ms.
* The edge service site based on both of the status it will be the
edge site 3, the E2E delay=19.4ms.
So, the best choice to ensure the E2E delay is edge service site 3,
which is 19.4ms and is less than 20ms. The differences of the E2E
delay is only 3~4ms among the three, but some of them will meet the
application demand while the others don't.
The conclusion is that E2E delay is a key factor for traffic
steering, and it is also necessary to consider whether an edge
service site has the required GPU resources. Even if a site offers
good latency, it cannot provide the service if it lacks GPU support.
For AR/VR scenarios, traffic should only be steered at the start of a
service session, because mid-session steering involves significant
context migration, which is costly and requires explicit application
participation and approval.
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Light Load Heavy Load Normal load
+------------+ +------------+ +------------+
| Edge | | Edge | | Edge |
| Site 1 | | Site 2 | | Site 3 |
+-----+------+ +------+-----+ +------+-----+
computing|delay(4ms) | computing|delay(5ms)
| computing|delay(10ms) |
+----+-----+ +-----+----+ +-----+----+
| Egress | | Egress | | Egress |
| Router 1 | | Router 2 | | Router 3 |
+----+-----+ +-----+----+ +-----+----+
newtork|delay(9ms) newtork|delay(4ms) newtork|delay(5ms)
| | |
| +--------+--------+ |
+-----------| Infrastructure |-----------+
+--------+--------+
|
+----+----+
| Ingress |
+---------------| Router |--------------+
| +----+----+ |
| | |
+--+--+ +--+---+ +---+--+
+------+| +------+ | +------+ |
|Client|+ |Client|-+ |Client|-+
+------+ +------+ +------+
client delay=1.5+7.9=9.4ms
Figure 3: Computing-Aware AR or VR
Furthermore, specific techniques may be employed to divide the
overall rendering into base assets that are common across a number of
clients participating in the service, while the client-specific input
data is being utilized to render additional assets. When being
delivered to the client, those two assets are being combined into the
overall content being consumed by the client. The requirements for
sending the client input data as well as the requests for the base
assets may be different in terms of which service instances may serve
the request, where base assets may be served from any nearby service
instance (since those base assets may be served without requiring
cross-request state being maintained), while the client-specific
input data is being processed by a stateful service instance that
changes, if at all, only slowly over time due to the stickiness of
the service that is being created by the client-specific data. Other
splits of rendering and input tasks can be found in [TR22.874] for
further reading.
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When it comes to the service instances themselves, those may be
instantiated on-demand, e.g., driven by network or client demand
metrics, while resources may also be released, e.g., after an idle
timeout, to free up resources for other services. Depending on the
utilized node technologies, the lifetime of such "function as a
service" may range from many minutes down to millisecond scale.
Therefore, computing resources across participating edges exhibit a
distributed (in terms of locations) as well as dynamic (in terms of
resource availability) nature. In order to achieve a satisfying
service quality to end users, a service request will need to be sent
to and served by an edge with sufficient computing resource and a
good network path.
4.2. Example 2: Computing-aware Intelligent Transportation
Urban intelligent transportation relies on a large number of high-
quality video capture devices, whose data needs to be processed at
edge service sites (e.g., pedestrian flow statistics, vehicle
tracking). This imposes stringent requirements on the computing
capabilities of edge service sites (for video processing) and network
performance (bandwidth, latency). CATS can address the issue by
coordinating network and computing resources.
In auxiliary driving scenarios (for example, "Extended Electronic
Horizon" [HORITA]), edge service sites collect road and traffic data
via V2X to address blind-spot and collision risks, and provide real-
time warnings and maneuver guidance. Requests are typically sent
preferentially to the closest edge node. However, if the closest
node becomes overloaded, it may lead to response delays and safety
risks, which requires CATS to perform traffic steering.
Specifically, delay-insensitive services (e.g., in-vehicle
entertainment) can be offloaded via CATS to edge service sites with
lighter loads (even if they are farther away), while delay-sensitive
assisted driving services are preferentially processed at local
service sites. As mentioned in the problem statement section, CATS
is an application-transparent network-layer solution. Unlike
ALTO[RFC7285], it enables coordinated scheduling of network and
computing resources without requiring application modifications. For
moving vehicles, CATS supports smooth and proactive context migration
between edge nodes, provided that the application allows it, to
maintain service continuity. In addition, vehicle speed is a key
factor: faster movement requires higher frequency of metric updates
(to be detailed in the requirements section) to ensure that CATS
steering decisions remain valid as vehicles switch services among
base stations or edge service sites.
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In video recognition scenarios, traffic surges (e.g., during rush
hours or weekends) can easily overload the closest edge service
sites. CATS addresses this scalability challenge by steering excess
service requests to other appropriate sites, ensuring that processing
capacity matches user demand.
4.3. Example 3: Computing-aware Digital Twin
A number of industry associations, such as the Industrial Digital
Twin Association or the Digital Twin Consortium
(https://www.digitaltwinconsortium.org/), have been founded to
promote the concept of the Digital Twin (DT) for a number of use case
areas, such as smart cities, transportation, industrial control,
among others. The core concept of the DT is the "administrative
shell" [Industry4.0], which serves as a digital representation of the
information and technical functionality pertaining to the "assets"
(such as an industrial machinery, a transportation vehicle, an object
in a smart city or others) that is intended to be managed,
controlled, and actuated.
As an example for industrial control, the programmable logic
controller (PLC) may be virtualized and the functionality aggregated
across a number of physical assets into a single administrative shell
for the purpose of managing those assets. PLCs may be virtualized in
order to move the PLC capabilities from the physical assets to the
edge cloud. Several PLC instances may exist to enable load balancing
and fail-over capabilities, while also enabling physical mobility of
the asset and the connection to a suitable "nearby" PLC instance.
With this, traffic dynamicity may be similar to that observed in the
connected car scenario in the previous subsection. Crucial here is
high availability and bounded latency since a failure of the
(overall) PLC functionality may lead to a production line stop, while
boundary violations of the latency may lead to loosing
synchronization with other processes and, ultimately, to production
faults, tool failures or similar.
Particular attention in Digital Twin scenarios is given to the
problem of data storage. Here, decentralization, not only driven by
the scenario (such as outlined in the connected car scenario for
cases of localized reasoning over data originating from driving
vehicles) but also through proposed platform solutions, such as those
in [GAIA-X], plays an important role. With decentralization,
endpoint relations between client and (storage) service instances may
frequently change as a result.
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In this use case, CATS is required for selecting the optimal PLC
instance and storage node, ensuring low latency and reliability for
data processing in industrial scenarios, as well as low latency for
data reading/writing during twin control processes.
4.4. Example 4: Computing-aware SD-WAN
SD-WAN is an overlay connectivity service that optimizes the
transport of IP packets over one or more underlay connectivity
services by recognizing applications and determining forwarding
behavior through the application of policies [MEF70.2]. SD-WAN can
be deployed by both service providers and enterprises to support
connectivity across branch sites, data centers, and cloud
environments. Applications or services may be deployed at multiple
locations to achieve performance, resiliency, or cost objectives.
In current SD-WAN deployments, forwarding decisions are primarily
based on network-related metrics such as available bandwidth,
latency, packet loss, or path availability. However, these decisions
typically lack visibility into the computing resources available at
the destination sites, such as CPU or GPU utilization, memory
pressure, or other composite cost metrics.
CATS metrics can complement existing SD-WAN network metrics by
providing information about the availability and condition of
computing resources associated with service instances at edge or
cloud sites. Such metrics may be consumed by a centralized SD-WAN
controller when deriving policies or computing preferred paths, and/
or by SD-WAN edge devices to make distributed, real-time traffic
steering decisions among already-deployed service instances. In both
cases, the goal is to enable application traffic to be steered
towards service instances and sites that best satisfy application
requirements by jointly considering network and computing conditions.
For the scenario of enterprises deploying applications in the cloud,
SD-WAN provides enterprises with centralized control over Customer-
Premises Equipments(CPEs) in branch offices and the cloudified
CPEs(vCPEs) in the clouds. The CPEs connect the clients in branch
offices and the application servers in clouds. The same application
server in different clouds is called an application instance.
Different application instances have different computing resource.
SD-WAN is aware of the computing resource of applications deployed in
the clouds by vCPEs, and selects the application instance for the
client to visit according to computing power and the network state of
WAN.
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Additionally, in order to provide cost-effective solutions, the SD-
WAN may also consider cost, e.g., in terms of energy prices incurred
or energy source used, when selecting a specific application instance
over another. For this, suitable metric information would need to be
exposed, e.g., by the cloud provider, in terms of utilized energy or
incurred energy costs per computing resource.
Figure 4 below illustrates Computing-aware SD-WAN for Enterprise
Cloudification.
+---------------+
+-------+ +----------+ | Cloud1 |
|Client1| /---------| WAN1 |------| vCPE1 APP1 |
+-------+ / +----------+ +---------------+
+-------+ +-------+
|Client2| ------ | CPE |
+-------+ +-------+ +---------------+
+-------+ \ +----------+ | Cloud2 |
|Client3| \---------| WAN2 |------| vCPE2 APP1 |
+-------+ +----------+ +---------------+
Figure 4: Illustration of Computing-aware SD-WAN for Enterprise
Cloudification
The current computing load status of the application APP1 in cloud1
and cloud2 is as follows: each application uses 6 vCPUs. The load of
application in cloud1 is 50%. The load of application in cloud2 is
20%. The computing resource of APP1 are collected by vCPE1 and vCPE2
respectively. Client1 and Client2 are visiting APP1 in cloud1. WAN1
and WAN2 have the same network states. Considering lightly loaded
application SD-WAN selects APP1 in cloud2 for the client3 in branch
office. The traffic of client3 follows the path: Client3 -> CPE ->
WAN2 -> Cloud2 vCPE1 -> Cloud2 APP1
4.5. Example 5: Computing-aware Distributed AI Training and Inference
Artificial Intelligence (AI) large model refers to models that are
characterized by their large size, high complexity, and high
computational requirements. AI large models have become increasingly
important in various fields, such as natural language processing for
text classification, computer vision for image classification and
object detection, and speech recognition.
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AI large model contains two key phases: training and inference.
Training refers to the process of developing an AI model by feeding
it with large amounts of data and optimizing it to learn and improve
its performance. On the other hand, inference is the process of
using the trained AI model to make predictions or decisions based on
new input data.
4.5.1. Distributed AI Inference
With the fast development of AI large language models, more
lightweight models can be deployed at edge service sites. Figure 5
shows the potential deployment of this case.
AI inference contains two major steps, prefilling and decoding.
Prefilling processes a user's prompt to generate the first token of
the response in one step. Following it, decoding sequentially
generates subsequent tokens step-by-step until the termination token.
These stages consume much computing resource. Important metrics for
AI inference are processor cores which transform prompts to tokens,
and memory resources which are used to store key-values and cache
tokens. The generation and processing of tokens indicates the
service capability of an AI inference system. Single site deployment
of the prefilling and decoding might not provide enough resources
when there are many clients sending requests (prompts) to access AI
inference service.
More generally, we also see the use of cost information, specifically
on the cost for energy expended on AI inferencing of the overall
provided AI-based service, as a possible criteria for steering
traffic. Here, we envision (AI) service tiers being exposed to end
users, allowing to prioritize, e.g., 'greener energy costs' as a key
criteria for service fulfilment. For this, the system would employ
metric information on, e.g., utilized energy mix at the AI inference
sites and costs for energy to prioritize a 'greener' site over
another, while providing similar response times.
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+----------------------------------------------------------+
| +--------------+ +--------------+ +--------------+ |
| | Edge | | Edge | | Edge | |
| | +----------+ | | +----------+ | | +----------+ | |
| | | Prefill | | | | Prefill | | | | Prefill | | |
| | +----------+ | | +----------+ | | +----------+ | |
| | +----------+ | | +----------+ | | +----------+ | |
| | | Decode | | | | Decode | | | | Decode | | |
| | +----------+ | | +----------+ | | +----------+ | |
| +--------------+ +--------------+ +--------------+ |
+----------+-----------------------------+-----------------+
| Prompt | Prompt
| |
+----+-----+ +-+--------+
| Client_1 | ... | Client_2 |
+----------+ +----------+
Figure 5: Illustration of Computing-aware AI large model inference
4.5.2. Distributed AI Training
Although large language models are nowadays confined to be trained
with very large centers with computational, often GPU-based,
resources, platforms for federated or distributed training are being
positioned, specifically when employing edge computing resources.
While those approaches apply their own (collective) communication
approach to steer the training and gradient data towards the various
(often edge) computing sites, we also see a case for CATS traffic
steering here. For this, the training clusters themselves may be
multi-site, i.e., combining resources from more than one site, but
acting as service instances in a CATS sense, i.e., providing the
respective training round as a service to the overall distributed/
federated learning platform.
One (cluster) site can be selected over another based on compute,
network but also cost metrics, or a combination thereof. For
instance, training may be constrained based on the network resources
to ensure timely delivery of the required training and gradient
information to the cluster site, while also computational load may be
considered, particularly when the cluster sites are multi-homed, thus
hosting more than one application and therefore become (temporarily)
overloaded. But equally to our inferencing use case in the previous
section, the overall training service may also be constrained by
cost, specifically energy aspects, e.g., when positioning the service
utilizing the trained model is advertising its 'green' credentials to
the end users. For this, costs based on energy pricing (over time)
as well as the energy mix may be considered. One could foresee, for
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instance, the coupling of surplus energy in renewable energy
resources to a cost metric upon which traffic is steered preferably
to those cluster sites that are merely consuming surplus and not grid
energy.
Storage is also necessary for performing distributed/federated
learning due to several key reasons. Firstly, it is needed to store
model checkpoints produced throughout the training process, allowing
for progress tracking and recovery in case of interruptions.
Additionally, storage is used to keep samples of the dataset used to
train the model, which often come from distributed sensors such as
cameras, microphones, etc. Furthermore, storage is required to hold
the models themselves, which can be very large and complex. Knowing
the storage performance metrics is also important. For instance,
understanding the I/O transfer rate of the storage helps in
determining the latency of accessing data from disk. Additionally,
knowing the size of the storage is relevant to understand how many
model checkpoints can be stored or the maximum size of the model that
can be locally stored.
4.6. Discussion
The five use cases mentioned in previous sections serve as examples
to show that CATS are needed for traffic steering. Considering that
these use cases are enough to derive common requirements, this
document only includes the aforementioned five use cases in the main
body, although there have been more similar use cases proposed in
CATS working group[I-D.dcn-cats-req-service-segmentation]. CATS has
raised strong interests in many other standardization bodies, such as
ETSI, 3GPP. The applicability of CATS may be further extended in
future use cases. At the mean time, the CATS framework may also need
to be modified or enhanced according to new requirements raised by
potential new CATS use cases. These potential use cases are not
included in the current document main body, but are attached in the
appendix A of this document.
5. Requirements
In the following, we outline the requirements for the CATS system to
overcome the observed problems in the realization of the use cases
above.
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5.1. Support Dynamic and Effective Selection among Multiple Service
Instances
The basic requirement of CATS is to support the dynamic access to
different service instances residing in multiple computing sites and
then being aware of their status, which is also the fundamental model
to enable the traffic steering and to further optimize the network
and computing services. A unique CATS service identifier (CS-ID) is
used by all the service instances for a specific service no matter
which edge service site an instance may attach to. The mapping of
this CS-ID to a network locator is basic to steer traffic to any of
the service instances deployed in various edge service sites.
Moreover, according to CATS use cases, some applications require E2E
low latency, which warrants a quick mapping of the service identifier
to the network locator. This leads to naturally the in-band methods,
involving the consideration of using metrics that are oriented
towards compute capabilities and resources, and their correlation
with services. Therefore, a desirable system
R1: MUST provide a dynamic discovery and resolution method for
mapping CS-ID to one or more current service instance addresses,
based on up-to-date system state.
R2: MUST provide a method to dynamically assess the availability of
service instances, based on up-to-date status metrics (e.g., health,
load, reachability).
Note: The term "up-to-date" herein refers to the latest metric
information collected by the system in accordance with the preset
metric update cycle. The principle for setting the cycle is
generally pre-determined by the network. For example, based on
historical statistical data, a relatively appropriate update cycle
(either second-level or millisecond-level) is selected for a specific
type or certain types of services.
5.2. Support Agreement on Metric Representation and Definition
Computing metrics can have many different semantics, particularly for
being service-specific. Even the notion of a "computing load" metric
could be represented in many different ways. Such representation may
entail information on the semantics of the metric or it may be purely
one or more semantic-free numerals. Agreement of the chosen
representation among all service and network elements participating
in the service instance selection decision is important. Therefore,
a desirable system
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R3: The implementations MUST agree on using metrics that are oriented
towards compute capabilities and resources and their representation
among service instances in the participating edges, at both design
time and runtime.
To better understand the meaning of different metrics and to better
support appropriate use of metrics,
R4: An information model of the compute and network resources MUST be
defined. Such a model MUST characterize how metrics are abstracted
out from the compute and network resources. We refer to this
information model as the Resource Model.
R5: The Resource Model MUST be implementable in an interoperable
manner. That is, metrics generated by this resource model MUST be
understood and interoperable across independent CATS implementations.
R6: The Resource Model MUST be executable in a scalable manner. That
is, the Resource Model MUST be capable of being executed at the
required time scale and at an affordable cost (e.g., memory
footprint, energy, etc.).
We recognize that different network nodes, e.g., routers, switches,
etc., may have diversified capabilities even in the same routing
domain, let alone in different administrative domains and from
different vendors. Therefore, to work properly in a CATS system,
R7: CATS systems MUST support staleness handling for CATS metrics and
provide indications of when metrics should be refreshed, so that CATS
components can know if a metric value is valid or not.
R8: All metric information used in CATS MUST be produced and encoded
in a format that is understood by participating CATS components. For
metrics that CATS components do not understand or support, CATS
components will ignore them.
R9: CATS MAY be applied in non-CATS network environments when needed,
considering that CATS is designed with extensibility and could work
compatibly with existing non-CATS network environments when the
network components in these environments could be upgraded to know
the meaning of CATS metrics.
R10: CATS components SHOULD support a mechanism to advertise or
negotiate supported metric types and encodings to ensure
compatibility across implementations.
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R11: The computation and use of metrics in CATS MUST be designed to
avoid introducing routing loops or path oscillations when metrics are
distributed and used for path selection.
Compute metrics can change rapidly, which may lead to path
oscillation if metrics are updated too frequently or become stale if
updated too infrequently. R10 ensures that CATS components can
negotiate metric types for consistent interpretation, while R11
requires that metrics be used in a way that avoids routing loops and
path instability. Together, they balance responsiveness with
stability.
5.3. Use of CATS Metrics
Network path costs in the current routing system usually do not
change very frequently. Network traffic engineering metrics (such as
available bandwidth) may change more frequently as traffic demands
fluctuate, but distribution of these changes is normally damped so
that only significant changes cause routing protocol messages.
However, metrics that are oriented towards compute capabilities and
resources in general can be highly dynamic, e.g., changing rapidly
with the number of sessions, the CPU/GPU utilization and the memory
consumption, etc. Service providers must determine at what interval
or based on what events such information needs to be distributed.
Overly frequent distribution with more accurate synchronization may
result in unnecessary overhead in terms of signaling.
Moreover, depending on the service related decision logic, one or
more metrics need to be conveyed in a CATS domain. The problem to be
addressed here may be the frequency of such conveyance, and which
CATS component is the decision maker for the service instance
selection should also be considered. Thereby, choosing appropriate
protocols for conveying CATS metrics is important. While existing
routing protocols may serve as a baseline for signaling metrics, for
example, BGP extensions[RFC4760] and GRASP[RFC8990]. These routing
protocols may be more suitable for distributed systems. Considering
about some centralized approaches to select CATS service instances,
other means to convey the metrics can equally be chosen and even be
realized, for example, leveraging restful API for publication of CATS
metrics to a centralized decision maker. Specifically, a desirable
system,
R12: MUST provide mechanisms for metric collection, including
specifying the responsible entity for collection.
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Collecting metrics from all of the services instances may incur much
overhead for decision makers. Hierarchical aggregation helps reduce
this burden by consolidating metrics at intermediate nodes, providing
a more scalable and efficient view of resource conditions.
CATS components do not need to be aware of how metrics are collected
behind the aggregator. The decision point may not be directly
connected with service instances or metric collectors, therefore,
R13: MUST provide mechanisms to distribute the metrics.
There may be various update frequencies for different computing
metrics. Some of the metrics may be more dynamic, while others are
relatively static. Accordingly, different distribution methods may
need to be chosen with respect to different update frequencies of
different metrics. Therefore a system,
For example, In highly mobile scenarios, such as fast-moving vehicles
mentioned in Section 4.2, compute metrics can quickly become outdated
as the UE moves across base stations and edge service sites,
potentially requiring more frequent updates. However, updates should
remain stable and avoid excessive overhead.
R14: MUST NOT be sensitive to the update frequency of the metrics,
and MUST NOT be dependent on or vulnerable to the mechanisms used to
distribute the metrics.
5.4. Support Instance Affinity
In the CATS system, a service may be provided by one or more service
instances that would be deployed at different locations in the
network. Each instance provides equivalent service functionality to
its respective clients. The decision logic of the instance selection
is subject to the packet level communication and packets are
forwarded based on the operating status of both network and computing
resources. This resource status will likely change over time,
leading to individual packets potentially being sent to different
network locations, possibly segmenting individual service
transactions and breaking service-level semantics. Moreover, when a
client moves, the access point might change and successively lead to
the migration of service instances. If execution changes from one
(e.g., virtualized) service instance to another, state/context needs
to be transferred to the new instance. Such required transfer of
state/context makes it desirable to have instance affinity as the
default, removing the need for explicit context transfer, while also
supporting an explicit state/context transfer (e.g., when metrics
change significantly).
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The nature of this affinity is highly dependent on the nature of the
service, which could be seen as an 'instance affinity' to represent
the relationship. The minimal affinity of a single request
represents a stateless service, where each service request may be
responded to without any state being held at the service instance for
fulfilling the request.
Providing any necessary information/state in the manner of in-band as
part of the service request, e.g., in the form of a multi-form body
in an HTTP request or through the URL provided as part of the
request, is one way to achieve such stateless nature.
Alternatively, the affinity to a particular service instance may span
more than one request, as in the AR/VR use case, where the previous
client input is needed to render subsequent frames.
However, a client, e.g., a mobile UE, may have many applications
running. If all, or majority, of the applications request the CATS-
based services, then the runtime states that need to be created and
accordingly maintained would require high granularity. In the
extreme scenario, this granular requirement could reach the level of
per-UE, per-APP, and per-(sub)flow with regard to a service instance.
Evidently, these fine-granular runtime states can potentially place a
heavy burden on network devices if they have to dynamically create
and maintain them. On the other hand, it is not appropriate either
to place the state-keeping task on clients themselves.
Besides, there might be the case that UE moves to a new (access)
network or the service instance is migrated to another cloud, which
cause the unreachable or inconvenient of the original service
instance. So the UE and service instance mobility also need to be
considered.
Therefore, a desirable system,
R15: CATS systems MUST maintain instance affinity for stateful
sessions and transactions on a per-flow basis.
R16: MUST avoid maintaining per-flow states for specific applications
in network nodes for providing instance affinity.
R17: SHOULD support service continuity in the presence of UE or
service instance mobility.
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5.5. Preserve Communication Confidentiality
Exposing CATS metrics to the network may lead to the leakage of
application privacy. In order to prevent it, it is necessary to
consider the methods to handle the sensitive information. For
instance, using general anonymization methods, including hiding the
key information representing the identification of devices, or using
an index to represent the service level of computing resources, or
using customized information exposure strategies according to
specific application requirements or network scheduling requirements.
At the same time, when anonymity is achieved, it is important to
ensure that the exposed computing information remains sufficient to
enable effective traffic steering. Therefore, a CATS system
R18: MUST preserve the confidentiality of the communication relation
between a user and a service provider by minimizing the exposure of
user-relevant information according to user's demands.
5.6. Correlation between Use Cases and Requirements
A table is presented in this section to better illustrate the
correlation between CATS use cases and requirements, 'X' is for
marking that the requirement can be derived from the corresponding
use case.
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+-------------------------------------------------+
| | Use cases |
+--Requirements--+-----+-----+------+------+------+
| |AR/VR| ITS | DT |SD-WAN| AI |
+-----------+----+-----+-----+------+------+------+
| Instance | R1 | X | X | X | X | X |
| Selection +----+-----+-----+------+------+------+
| | R2 | X | X | X | X | X |
+-----------+----+-----+-----+------+------+------+
| | R3 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R4 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| Metric | R5 | X | X | X | X | X |
|Definition +----+-----+-----+------+------+------+
| | R6 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R7 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R8 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R9 | X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R10| X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R11| X | X | X | X | X |
+-----------+----+-----+-----+------+------+------+
| | R12| X | X | X | X | X |
| +----+-----+-----+------+------+------+
| Use of | R13| X | X | X | X | X |
| Metrics +----+-----+-----+------+------+------+
| | R14| X | X | X | X | X |
+-----------+----+-----+-----+------+------+------+
| | R15| X | X | X | X | X |
| Instance +----+-----+-----+------+------+------+
| Affinity | R16| X | X | X | X | X |
| +----+-----+-----+------+------+------+
| | R17| X | X | | | X |
+-----------+----+-----+-----+------+------+------+
| Confiden- | R18| X | X | X | X | X |
| -tiality | | | | | | |
+-----------+----+-----+-----+------+------+------+
Figure 6: Mapping between CATS Use Cases and Requirements
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6. Security Considerations
CATS decision-making relies on real-time computing and network status
as well as service information, requiring robust security safeguards
to mitigate risks associated with dynamic service and resource
scheduling, and cross-node data transmission.
Core Security Risks and Requirements include:
* User Privacy Leakage Risk
Description: CATS involves user-related data (e.g., access patterns,
service requests) across edge service sites. Unauthorized disclosure
of user identifiers or per-user behavior tracking risks profiling or
identity theft, especially in use cases with personal/context-rich
data (e.g., AR/VR, vehicle trajectories, AI prompts), violating
regulations and eroding trust.
R19: User activity privacy MUST be preserved by anonymizing
identifying information. Per-user behavior pattern tracking is
prohibited.
* Service Instance Identity Spoofing and Traffic Hijacking
Description: Attackers may spoof legitimate service instance
identities or tamper with "CS-ID-instance address" mappings (per R1),
diverting traffic to malicious nodes. This undermines CATS' core
scheduling logic, causing service disruptions, data leaks, and
potential physical harm in safety-critical scenarios.
R20: Service instances MUST be authenticated. "CS-ID - instance
address" mapping results MUST be encrypted.
* Tampering and False Reporting of CATS Metrics
Description: Attackers may tamper with core scheduling metrics or
submit false data (per R3-R17), misleading traffic steering
decisions. This leads to node overload, link congestion, or
"resource exhaustion attacks," directly degrading Quality of
Experience (QoE).
R21: Metric collection and distribution MUST employ encryption.
Mechanisms for secondary validation and traceability of abnormal
metrics MUST be supported, avoiding over-reliance on single-node
reports.
* Security of Cross-Node Context Migration Data
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Description: During user or terminal mobility, session states and
computing context (e.g., AR rendering progress, vehicle status) may
be intercepted or tampered with during cross-node migration (per
R18-R22). This impairs service continuity, leaks sensitive data, or
causes state inconsistency.
R22: Migration data MUST use end-to-end encryption, accessible only
to authorized target instances. Migration instructions MUST include
integrity check codes.
7. IANA Considerations
This document makes no requests for IANA action.
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC4760] Bates, T., Chandra, R., Katz, D., and Y. Rekhter,
"Multiprotocol Extensions for BGP-4", RFC 4760,
DOI 10.17487/RFC4760, January 2007,
<https://www.rfc-editor.org/info/rfc4760>.
[RFC4786] Abley, J. and K. Lindqvist, "Operation of Anycast
Services", BCP 126, RFC 4786, DOI 10.17487/RFC4786,
December 2006, <https://www.rfc-editor.org/info/rfc4786>.
[RFC7285] Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
"Application-Layer Traffic Optimization (ALTO) Protocol",
RFC 7285, DOI 10.17487/RFC7285, September 2014,
<https://www.rfc-editor.org/info/rfc7285>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
[RFC8990] Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "GeneRic
Autonomic Signaling Protocol (GRASP)", RFC 8990,
DOI 10.17487/RFC8990, May 2021,
<https://www.rfc-editor.org/info/rfc8990>.
8.2. Informative References
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[GAIA-X] Gaia-X, "GAIA-X: A Federated Data Infrastructure for
Europe", 2021.
[HORITA] Horita, Y., "Extended electronic horizon for automated
driving", Proceedings of 14th International Conference on
ITS Telecommunications (ITST), 2015.
[I-D.dcn-cats-req-service-segmentation]
Ngọc, T. M. and Y. Kim, "Additional CATS requirements
consideration for Service Segmentation-related use cases",
Work in Progress, Internet-Draft, draft-dcn-cats-req-
service-segmentation-02, 1 July 2025,
<https://datatracker.ietf.org/doc/html/draft-dcn-cats-req-
service-segmentation-02>.
[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-19, 20 November 2025,
<https://datatracker.ietf.org/doc/html/draft-ietf-cats-
framework-19>.
[Industry4.0]
Industry4.0, "Details of the Asset Administration Shell,
Part 1 & Part 2", 2020.
[MEF70.2] MEF, Ed., "SD-WAN Service Attributes and Service
Framework", 2023.
[TR22.874] 3GPP, "Study on traffic characteristics and performance
requirements for AI/ML model transfer in 5GS (Release
18)", 2021.
Appendix A. Appendix A
This section presents an additional CATS use case, which is not
included in the main body of this document. Reasons are that the use
case may bring new requirements that are not considered in the
initail charter of CATS working group. The requirements impact the
design of CATS framework and may need further modificaition or
enhancement on the initial CATS framework that serves all the
existing use cases listed in the main body. However, the ISAC use
case is promising and has gained industry consensus. Therefore, this
use case may be considered in future work of CATS working group.
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A.1. Integrated Sensing and Communications (ISAC)
Integrated Sensing and Communications (ISAC) enables wireless
networks to perform simultaneous data transmission and environmental
sensing. In a distributed sensing scenario, multiple network nodes
--such as base stations, access points, or edge devices-- collect raw
sensing data from the environment. This data can include radio
frequency (RF) reflections, Doppler shifts, channel state information
(CSI), or other physical-layer features that provide insights into
object movement, material composition, or environmental conditions.
To extract meaningful information, the collected raw data must be
aggregated and processed by a designated computing node with
sufficient computational resources. This requires efficient
coordination between sensing nodes and computing resources to ensure
timely and accurate analysis, making it a relevant use case for
Computing-Aware Traffic Steering (CATS) in IETF.
This use case aligns with ongoing efforts in standardization bodies
such as the ETSI ISAC Industry Specification Group (ISG),
particularly Work Item #5 (WI#5), titled 'Integration of Computing
with ISAC'. WI#5 focuses on exploring different forms of computing
integration within ISAC systems, including sensing combined with
computing, communications combined with computing, and the holistic
integration of ISAC with computing. The considerations outlined in
this document complement ETSI's work by examining how computing-aware
networking solutions, as developed within CATS, can optimize the
processing and routing of ISAC sensing data.
As an example, we can consider a network domain with multiple sites
capable of hosting the ISAC computing "service", each with
potentially different connectivity and computing characteristics.
Figure 7 shows an exemplary scenario. Considering the connectivity
and computing latencies (just as an example of metrics), the best
service site is #n-1 in the example used in the Figure. Note that in
the figure we stilluse 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.
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_______________
( -------- )
( | | )
( -------- | )
________________ ( | | | ) ________________
( -------- ) ( --------- | | ) ( -------- )
( | | ) ( |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 )
( // )
( // )
( // )
( // )
( // )
( ------- ------- )
-------| ICR#1 |---------------------| ICR#2 |--------
------- __ -------
(.) (.) / ( ) (.)
(.) ----- - ( ) (.)
(.) | UE2 | / (__) \ (.)
(.) ----- / - -----
(.) / (sensing) \ | UE3 |
----- ----------- -----
| UE1 | /
-----
Figure 7: Exemplary ISAC Scenario
In the distributed sensing use case, the sensed data collected by
multiple nodes must be efficiently routed to a computing node capable
of processing it. The choice of the computing node depends on
several factors, including computational load, network congestion,
and latency constraints. CATS mechanisms can optimize the selection
of the processing node by dynamically steering the traffic based on
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computing resource availability and network conditions.
Additionally, as sensing data is often time-sensitive, CATS can
ensure low-latency paths while balancing computational demands across
different processing entities. This capability is essential for
real-time applications such as cooperative perception for autonomous
systems, industrial monitoring, and smart city infrastructure.
A.1.1. Requirements
In addition to some of the requirements already identified for CATS
in the main body of this document, there are several additional
challenges and requirements that need to be addressed for efficient
distributed sensing in ISAC-enabled networks:
CATS systems should be able to select an instance where multiple
nodes can steer traffic to simultaneously, ensuring that packets
arrive within a maximum time period. This is required because there
are distributed tasks in which there are multiple nodes acting as
sensors that produce sensing data that has to be then processed by a
sensing processing function, typically hosted at the edge. This
implies that there is a multi-point to point kind of direction of the
traffic, with connectivity and computing requirements associated
(which can be very strict for some types of sensing schema).
CATS systems should provide mechanisms that implement per node/flow
security and privacy policies to adapt to the nature of the sensitive
information that might be exchanged in a sensing task.
Acknowledgements
The authors would like to thank Adrian Farrel, Peng Liu, Joel
Halpern, Jim Guichard, Cheng Li, Luigi Iannone, Christian Jacquenet,
Yuexia Fu, Erum Welling, Ines Robles, Linda Dunbar, and Jim Reid for
their valuable suggestions to this document.
The authors would like to thank Yizhou Li for her early IETF work of
Compute First Network (CFN) and Dynamic Anycast (Dyncast) which
inspired the CATS work.
Contributors
The following people have substantially contributed to this document:
Yizhou Li
Huawei Technologies
Email: liyizhou@huawei.com
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Dirk Trossen
Email: dirk@trossen.tech
Mohamed Boucadair
Orange
Email: mohamed.boucadair@orange.com
Carlos J. Bernardos
UC3M
Email: cjbc@it.uc3m.es
Peter Willis
Email: pjw7904@rit.edu
Philip Eardley
Email: ietf.philip.eardley@gmail.com
Tianji Jiang
China Mobile
Email: tianjijiang@chinamobile.com
Minh-Ngoc Tran
ETRI
Email: mipearlska@etri.re.kr
Markus Amend
Deutsche Telekom
Email: Markus.Amend@telekom.de
Guangping Huang
ZTE
Email: huang.guangping@zte.com.cn
Dongyu Yuan
ZTE
Email: yuan.dongyu@zte.com.cn
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Xinxin Yi
China Unicom
Email: yixx3@chinaunicom.cn
Tao Fu
CAICT
Email: futao@caict.ac.cn
Jordi Ros-Giralt
Qualcomm Europe, Inc.
Email: jros@qti.qualcomm.com
Jaehoon Paul Jeong
Sungkyunkwan University
Email: pauljeong@skku.edu
Yan Wang
Migu Culture Technology Co.,Ltd
Email: wangyan_hy1@migu.chinamobile.com
Authors' Addresses
Kehan Yao
China Mobile
Email: yaokehan@chinamobile.com
Luis M. Contreras
Telefonica
Email: luismiguel.contrerasmurillo@telefonica.com
Hang Shi
Huawei Technologies
Email: shihang9@huawei.com
Shuai Zhang
China Unicom
Email: zhangs366@chinaunicom.cn
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Qing An
Alibaba Group
Email: anqing.aq@alibaba-inc.com
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