A Task Segmentation Framework for Computing-Aware Traffic Steering
draft-li-cats-task-segmentation-framework-02
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Chuanhuang Li , Junhao Wan , Huaqing Tu , Chuanyang Miao , Yingjie Yu , Shicong Zhang , Bo Ma | ||
| Last updated | 2026-07-20 | ||
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draft-li-cats-task-segmentation-framework-02
cats C. Li
Internet-Draft J. Wan
Intended status: Informational H. Tu
Expires: 22 January 2027 Zhejiang Gongshang University
C. Miao
ZTE Corporation
Y. Yu
Zhejiang Gongshang University
S. Zhang
China Mobile
B. Ma
Zhejiang Gongshang University
21 July 2026
A Task Segmentation Framework for Computing-Aware Traffic Steering
draft-li-cats-task-segmentation-framework-02
Abstract
This document proposes an extension to the Computing-Aware Traffic
Steering (CATS) framework by introducing a task segmentation module.
This extension enables the CATS framework to handle divisible
computing requests by breaking them down into smaller computational
units, referred to as "Task". The document focuses on two primary
task segmentation modes: the distributed mode and the chain-
structured Directed Acyclic Graph (DAG) model, providing detailed
workflows for each.
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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Task Force (IETF). Note that other groups may also distribute
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 22 January 2027.
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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
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provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Requirements Language . . . . . . . . . . . . . . . . . . . . 3
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Task Segmentation Module (TSM) . . . . . . . . . . . . . . . 4
4.1. Divisible and Indivisible Tasks . . . . . . . . . . . . . 4
4.2. Classification of Divisible Tasks . . . . . . . . . . . . 4
4.3. Task Segmentation Process . . . . . . . . . . . . . . . . 5
4.4. Application Scenarios for Task Segmentation . . . . . . . 6
5. The Role of Task Segmentation in the CATS Framework . . . . . 7
6. CATS Workflow under Multi-Task Segmentation . . . . . . . . . 7
6.1. Distributed Mode . . . . . . . . . . . . . . . . . . . . 7
6.2. Chain-Structured DAG Model . . . . . . . . . . . . . . . 10
7. Security Considerations . . . . . . . . . . . . . . . . . . . 12
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1. Normative References . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . 12
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
With the growing demand for efficient and scalable network services,
segmenting computational tasks for distributed processing has become
increasingly important. This document extends the existing CATS
framework by introducing a "Task Segmentation Module" (TSM), which
collaborates with the CATS Service Metric Agent (C-SMA) and the CATS
Network Metric Agent (C-NMA) to support task-aware traffic steering,
including service contact instance selection and CATS-computed path
selection.
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2. Requirements Language
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. Terminology
This document makes use of the terms defined in
[I-D.ietf-cats-framework]. In addition, the following terms are
defined below:
CATS Service Metric Agent (C-SMA): A functional entity that is
responsible for collecting service capabilities and status, and
for reporting them to a CATS Path Selector (C-PS). In the task
segmentation scenarios described in this document, C-SMA
information can be provided in a distributed manner by C-SMA
functions associated with service sets or service sites, rather
than by a single centralized function for all service sites.
(From [I-D.ietf-cats-framework])
CATS Network Metric Agent (C-NMA): A functional entity that is
responsible for collecting network capabilities and status, and
for reporting them to a C-PS. (From [I-D.ietf-cats-framework])
CATS Path Selector (C-PS): A functional entity that selects paths
towards service sites and instances, and thus towards service
contact instances, in order to accommodate the requirements of
service requests. The path selection engine takes into account
service and network status information. (From
[I-D.ietf-cats-framework])
Task: In this document, a Task refers to a logical computational
unit within a service request that can be processed independently
or as a stage in a chain-structured DAG model. The granularity of
a task is smaller than that of a service and represents specific
operations executed within a service instance.
Task Identifier: A Task Identifier is used to distinguish an
original service request from the subtasks derived from it. An
indivisible task uses a single task identifier. A divisible task
can use suffixes to identify subtasks derived from the original
task identifier, for example 2.1 and 2.2 for two subtasks derived
from task 2.
Computing Power Brain (CPB): The Computing Power Brain (CPB) is
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responsible for coordinating C-NMA and C-SMA information and
transmitting task segmentation instructions to the C-TEA.
CATS Task Execution Agent (C-TEA): The CATS Task Execution Agent
(C-TEA) is responsible for receiving task data and determining the
task segmentation method and optimal path. It receives task
segmentation instructions transmitted by the CPB, transforms
logical tasks into actionable task units, and interacts with the
CATS Path Selector (C-PS) and the CATS-Forwarder to ensure task-
related traffic is steered to the appropriate service sites. The
term execution in C-TEA refers to the application of task
segmentation and steering instructions in this document; it does
not require CATS to execute the application computation itself.
Task Segmentation Module (TSM): The Task Segmentation Module (TSM),
primarily composed of the CPB and C-TEA, is a functional component
tasked with analyzing incoming requests and segmenting them into
smaller task units based on their dependencies and independence.
For further details, see Section 4.
4. Task Segmentation Module (TSM)
To enhance the utilization of computing and network resources, this
document extends the CATS framework by introducing the concept of
task segmentation. Task segmentation involves breaking down received
service requests into smaller computational units to enable more
efficient distribution and processing across network and computing
resources. Tasks can be categorized into two types based on their
characteristics: divisible tasks and indivisible tasks.
4.1. Divisible and Indivisible Tasks
Divisible Tasks: These refer to computational requests that are
still divisible when they are received. A divisible task can be
represented as multiple subtasks. The subtasks can either be
processed non-dependently or executed sequentially. A divisible
task can retain the identifier of the original task and add
suffixes for subtasks, for example task 2.1 and task 2.2.
Indivisible Tasks: These are computational requests that have
already reached the minimum processing granularity when they are
received and cannot be broken into subtasks. An indivisible task
is processed as a whole and uses a single task identifier.
4.2. Classification of Divisible Tasks
Divisible tasks can be further divided into dependent tasks and Non-
dependent tasks.
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Dependent Tasks: These tasks exhibit a defined sequence of execution
and dependencies among subtasks. For example, the output of Task
A serves as the input for Task B. Traffic steering decisions,
service contact instance selection, and CATS-computed path
selection must preserve these dependencies so that task-related
traffic follows the required order, consistent with computational
logic and data-flow requirements.
Non-dependent Tasks: These tasks can be divided into relatively
autonomous atomic tasks, each of which can be completed Non-
dependently without relying on the results of other tasks. Non-
dependent tasks are suitable for parallel processing,
significantly improving computational efficiency.
4.3. Task Segmentation Process
This section describes the task segmentation process. The C-TEA
determines the task segmentation method and optimal path, while the
CPB transmits the corresponding segmentation instructions.
Task Reception: The CPB receives computational requests from clients
and provides the related task data to the C-TEA.
Task Analysis: Based on the characteristics and requirements of the
request, the C-TEA determines whether the task is divisible. If
the task is indivisible, it is treated as a whole and uses a
single task identifier.
Task Identifier Assignment: If the task is divisible, the C-TEA or
the task segmentation logic can identify subtasks by adding
suffixes to the original task identifier. For example, an
original task 2 can be represented as task 2.1 and task 2.2 after
segmentation.
Task Classification: If the task is divisible, the C-TEA further
analyzes whether it involves task dependencies. If the task is
indivisible, the request is steered as a whole towards a suitable
service contact instance, as described in I-D.ietf-cats-framework.
Traffic Steering and Service Selection: When a task is divisible,
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traffic associated with dependent tasks is steered sequentially
between service sites according to their dependencies. Traffic
associated with non-dependent tasks can be steered to different
service contact instances for parallel processing. The C-TEA
determines task segmentation and CATS-computed paths using C-SMA
and C-NMA information. The CPB transmits the corresponding
segmentation instructions, and the C-TEA applies the task
representation. Specific segmentation details, including
segmentation ratio and the number of subtasks, are beyond the
scope of this document.
4.4. Application Scenarios for Task Segmentation
Task segmentation significantly optimizes traffic steering and
resource utilization within the CATS framework. This section
provides the original dependent and non-dependent categories and adds
a concrete example for a chain-structured DAG model.
Applications for Dependent Tasks: These are suitable for scenarios
requiring sequential execution, such as data stream computing or
complex pipeline processing, e.g., multi-step encoding and
compression in video processing.
Agent-Based Enterprise AI Assistant: A concrete example of a chain-
structured DAG model is an agent-based enterprise AI assistant.
For example, a user can submit a request to generate an answer
based on enterprise policy documents. This service request can be
represented as a sequence of dependent subtasks. The first
subtask performs intent analysis and identifies the user intent,
required knowledge domain, and possible tool requirements. The
second subtask performs knowledge retrieval or tool selection
based on the output of the intent analysis subtask. The third
subtask performs reasoning and answer planning based on the
retrieved information or tool results. The fourth subtask
generates the final response. These subtasks form a chain-
structured DAG model because each subtask depends on the output of
the previous subtask. Knowledge retrieval cannot be performed
correctly before the intent is identified, reasoning cannot be
performed before the relevant knowledge or tool result is
available, and response generation depends on the reasoning
result. Therefore, the service request can be represented as Task
1 -> Task 2 -> Task 3 -> Task 4.
In this scenario, the application layer can describe the logical
task chain by agent names, such as an intent analysis agent, a
retrieval agent, a reasoning agent, and a response generation
agent. The CATS-enabled network does not execute these agents.
Instead, CATS uses the task model, service status, and network
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status to select suitable service sites and CATS-computed paths
for traffic associated with each subtask. If multiple instances
of the same agent are available, the concrete instance selection
can be handled by a service gateway or internal service routing
policy within the service set.
Applications for Non-dependent Tasks: These are ideal for large-
scale parallel computing, such as batch image processing or
distributed data analysis tasks. By representing non-dependent
tasks, CATS can select suitable service contact instances and
CATS-computed paths for traffic associated with parallel subtasks,
based on service and network status.
5. The Role of Task Segmentation in the CATS Framework
Tasks serve as the fundamental units for representing service
requests in task-aware CATS steering. By segmenting requests into
tasks, the CATS framework can optimize resource utilization and path
selection in a more granular manner based on the current state of
network and computing resources. The introduction of tasks enables
the CATS framework to achieve the following:
Optimizing Path and Service Selection: Through task segmentation and
task representation, CATS can select suitable service contact
instances and CATS-computed paths, thereby enhancing the
utilization of both computing and network resources.
Supporting Parallel Steering: For divisible non-dependent tasks,
CATS can steer task-related traffic to service contact instances
that support parallel processing.
Supporting Dependency Management: In the chain-structured DAG model,
inter-task dependencies can be maintained to ensure the correct
order of computation.
6. CATS Workflow under Multi-Task Segmentation
6.1. Distributed Mode
In the distributed mode, a task is represented as non-dependent
subtasks, and traffic associated with those subtasks is steered to
multiple service contact instances for parallel processing. This
mode is suitable for tasks without dependencies.
As shown in Figure 1.The detailed workflow for distributed tasks is
as follows:
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(1) The client sends a service request. The CPB receives the
request and provides the related task data to the C-TEA, which
determines that the task type is non-dependent.
(2) The C-TEA uses service and network status information from the
C-SMA and C-NMA to determine the segmentation method and CATS-
computed paths. The CPB transmits the corresponding
segmentation instructions.
(3) The C-PS selects a CATS-computed path for each subtask. The
ingress CATS-Forwarder steers each subtask along its selected
path to the corresponding egress CATS-Forwarder and service
site.
(4) Each selected service contact instance processes its associated
subtask in parallel, and the results are aggregated and returned
to the client.
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+-----+
+------+| +-------+
|client|+------------------------>| CPB |
+---+--+ +-------+
| | ^ ^
| | | |
+---+---+ | | |
| C-TEA |<------------------------+ | |
+---+---+ | |
| | |
| | |
+-------------------+---------------------+ | |
| | | | |
| | | | |
+---------+------+ +---------+------+ +-----------+----+ | |
| |C-PS#1| | |C-PS#2| | |C-PS#3| | |
....| +------|..| +------|..| +------|.... | |
: |CATS-Forwarder 1| |CATS-Forwarder 2| |CATS-Forwarder 3| : | |
: +----------------+ +----------------+ +----------------+ : | |
: : | |
: : | |
: Underlay +-------+ : | |
: Infrastructure | C-NMA |----------+ |
: +-------+ : |
: : |
: : |
: : |
: +----------------+ +----------------+ +----------------+ : |
: |CATS-Forwarder 4| |CATS-Forwarder 5| |CATS-Forwarder 6| : +-------+
....| |..| |..| |.... | C-SMA |
+------+---------+ +--------+-------+ +--------+-------+ +-------+
| | | ^ ^ ^
| | | | | |
| | | | | |
+------------+ +------------+ +------------+ | | |
+------------+ | +------------+ | +------------+ | | | |
| Service | | | Service | | | Service | | | | |
| Contact | |--+ | Contact | |--+ | Contact | |----+ | |
| Instance |-+ | | Instance |-+ | | Instance |-+ | |
+------------+ | +------------+ | +------------+ | |
Service Site 1 | Service Site 2 | Service Site 3 | |
| +--------------------------+ |
+----------------------------------------------------+
Figure 1: Distributed Framework (assumes segmentation into 3
subtasks)
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6.2. Chain-Structured DAG Model
The chain-structured DAG model is applicable for tasks with
interdependencies, where each subtask depends on the output of the
previous subtask and must be routed sequentially between service
sites. An example is an agent-based enterprise AI assistant, where a
user request is processed by a sequence of agent subtasks, such as
intent analysis, knowledge retrieval or tool selection, reasoning and
answer planning, and response generation.
As shown in Figure 2, the detailed workflow for the agent-based
enterprise AI assistant under the chain-structured DAG model is as
follows:
(1) The client sends an enterprise AI assistant request. The CPB
receives the service request and represents it as a chain of
dependent subtasks, for example Task 1 for intent analysis, Task
2 for knowledge retrieval or tool selection, Task 3 for
reasoning and answer planning, and Task 4 for response
generation.
(2) The C-TEA receives the data and determines the segmentation
method and optimal path. The CPB uses the network status and
service status provided by the C-SMA and C-NMA to transmit the
segmentation instructions.
(3) The C-TEA receives the instructions and represents the agent
subtasks as task units that can be used for CATS traffic
steering.
(4) The traffic associated with Task 1 is steered to the selected
service contact instance for intent analysis. After Task 1 is
completed, its output is used as the input of Task 2. The same
process is then applied, in the dependency order of the chain-
structured DAG model, to knowledge retrieval or tool selection,
reasoning and answer planning, and response generation.
(5) After the response generation subtask is completed, the final
result is returned to the client.
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+-----+
+------+| +-------+<-------------------------------------------------+
|client|+------------------------>| CPB | |
+---+--+ +-------+<-----------------------------------------------+ |
| | ^ | |
| | | | |
+---+---+<------------------------+ | | |
| C-TEA | | | |
+---+---+---------------------------------+ | |
| +-----------+ +---------+ +---------+ | |
| | | | | | | | |
+---------+------+ | +-------+--------+ | +-----+----------+ | +-----+----------+ | |
| |C-PS#1| | | |C-PS#2| | | |C-PS#3| | | |C-PS#4| | |
..| +------|.. | ..| +------|.. | ..| +------|.. | ..| +------|.. | |
: |CATS-Forwarder 1| : | : |CATS-Forwarder 2| : | : |CATS-Forwarder 3| : | : |CATS-Forwarder 4| : | |
: +----------------+ : | : +----------------+ : | : +----------------+ : | : +----------------+ : | |
: Underlay : | : Underlay : | : Underlay : | : Underlay : | |
: Infrastructure : | : Infrastructure : | : Infrastructure : | : Infrastructure : | |
: : | : : | : : | : : | |
: +-------+ : | : +-------+ : | : +-------+ : | : +-------+ : | |
: | C-NMA | : | : | C-NMA | : | : | C-NMA | : | : | C-NMA | : | |
: +---+---+ : | : +---+---+ : | : +---+---+ : | : +---+---+ : | |
: | : | : | : | : | : | : | : | |
: +----------:-|-:---------+----------:-|-:---------+----------:-|-:---------+----------:--|-+
: +----------------+ : | : +----------------+ : | : +----------------+ : | : +----------------+ : |
: |CATS-Forwarder 5| : | : |CATS-Forwarder 6| : | : |CATS-Forwarder 7| : | : |CATS-Forwarder 8| : |
..| |.. | ..| |.. | ..| |.. | ..| |.. |
+----------------+ | +----------------+ | +----------------+ | +----------------+ |
| | | | | | | |
| | | | | | | |
+------------+ | +------------+ | +------------+ | +------------+ |
+------------+ | | +------------+ | | +------------+ | | +------------+ | |
| Service | |------+ | Service | |-------+ | Service | |-------+ | Service | | |
| Contact | |--+ | Contact | |--+ | Contact | |--+ | Contact | |--+ |
| Instance |-+ | | Instance |-+ | | Instance |-+ | | Instance |-+ | |
+------------+ | +------------+ | +------------+ | +------------+ | |
Service Site 1 | Service Site 2 | Service Site 3 | Service Site 4 | |
| | | | |
| | | | |
+-------+<-+ +-------+<-+ +-------+<-+ +-------+<-+ |
| C-SMA | | C-SMA | | C-SMA | | C-SMA | |
+---+---+ +---+---+ +---+---+ +---+---+ |
| | | | |
+-----------------------+------------------------+------------------------+------------+
Figure 2: Chain-Structured DAG Framework (assumes segmentation
into 4 subtasks)
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7. Security Considerations
TBD
8. IANA Considerations
TBD
9. References
9.1. 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>.
[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>.
9.2. Informative References
[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>.
Acknowledgements
TBD
Authors' Addresses
Chuanhuang Li
Zhejiang Gongshang University
Zhejiang
China
Email: chuanhuang_li@zjgsu.edu.cn
Junhao Wan
Zhejiang Gongshang University
Zhejiang
China
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Email: 13970967804@163.com
Huaqing Tu
Zhejiang Gongshang University
Zhejiang
China
Email: thq@mail.zjgsu.edu.cn
Chuanyang Miao
ZTE Corporation
Nanjing
China
Email: miao.chuanyang@zte.com.cn
Yingjie Yu
Zhejiang Gongshang University
Zhejiang
China
Email: 1912190128@pop.zjgsu.edu.cn
Shicong Zhang
China Mobile
China
Email: zhangshicong@cmhi.chinamobile.com
Bo Ma
Zhejiang Gongshang University
Zhejiang
China
Email: mabo@zjgsu.edu.cn
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