



Computing-Aware Traffic Steering                                   Q. Li
Internet-Draft                                                   Z. Luan
Intended status: Informational                      Pengcheng Laboratory
Expires: 2 September 2026                                       Y. Jiang
  Tsinghua Shenzhen International Graduate School & Pengcheng Laboratory
                                                            1 March 2026


  A Framework for Compute-Aware Task Placement and Traffic Steering in
                    Heterogeneous Computing Networks
                       draft-luan-cats-catpts-00

Abstract

   The increasing deployment of geographically distributed computing
   infrastructures equipped with heterogeneous compute resources (e.g.,
   CPU, GPU, memory) has motivated new architectural approaches for
   jointly optimizing task placement and traffic steering.  In
   heterogeneous computing networks, tasks must be assigned to compute-
   capable nodes while respecting multi-dimensional resource constraints
   and network bandwidth limitations.

   This document presents a conceptual framework for Compute-Aware Task
   Placement and Traffic Steering (CATPTS).  The framework models a
   computing network as a directed graph containing compute-capable
   nodes and forwarding-only nodes.  Task execution location selection
   and two-stage traffic steering are jointly optimized under link
   bandwidth and multi-dimensional compute capacity constraints.  The
   objective is to achieve global load balancing across compute and
   network resources.

   This document defines the architectural principles, conceptual model,
   terminology, and optimization formulation underlying such systems.
   It does not specify protocol mechanisms.

About This Document

   This note is to be removed before publishing as an RFC.

   Status information for this document may be found at
   https://datatracker.ietf.org/doc/draft-luan-cats-catpts/.

   Discussion of this document takes place on the Computing-Aware
   Traffic Steering Working Group mailing list (mailto:cats@ietf.org),
   which is archived at https://mailarchive.ietf.org/arch/browse/cats/.
   Subscribe at https://www.ietf.org/mailman/listinfo/cats/.





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Status of This Memo

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Background and Architectural Motivation . . . . . . . . . . .   4
     2.1.  Heterogeneous Compute Networks  . . . . . . . . . . . . .   4
     2.2.  Coupling Between Task Placement and Traffic Steering  . .   4
   3.  Compute-Aware Task Routing Framework  . . . . . . . . . . . .   4
     3.1.  Design Principles . . . . . . . . . . . . . . . . . . . .   4
   4.  High-Level Architecture . . . . . . . . . . . . . . . . . . .   5
   5.  Network Model . . . . . . . . . . . . . . . . . . . . . . . .   5
   6.  Task Model  . . . . . . . . . . . . . . . . . . . . . . . . .   6
   7.  Resource Constraints  . . . . . . . . . . . . . . . . . . . .   6
     7.1.  Link Capacity Constraint  . . . . . . . . . . . . . . . .   6
     7.2.  Compute Capacity Constraint . . . . . . . . . . . . . . .   6
   8.  Optimization Objective  . . . . . . . . . . . . . . . . . . .   6
   9.  Applicability . . . . . . . . . . . . . . . . . . . . . . . .   7
   10. Security Considerations . . . . . . . . . . . . . . . . . . .   7



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   11. Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   7
   12. Informative References  . . . . . . . . . . . . . . . . . . .   8
   Appendix A.  IANA Considerations  . . . . . . . . . . . . . . . .   8
   Appendix B.  References . . . . . . . . . . . . . . . . . . . . .   8
     B.1.  Normative References  . . . . . . . . . . . . . . . . . .   8
     B.2.  Informative References  . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   Modern geographically distributed computing infrastructures integrate
   network transport and heterogeneous compute resources.  In many
   scenarios, tasks are generated at source nodes, processed at
   intermediate compute-capable nodes, and deliver results to
   destination nodes.  Examples include AI inference pipelines, edge-
   cloud collaboration, and distributed data processing.

   Traditional traffic engineering focuses on routing source-destination
   flows.  Traditional compute scheduling assumes tasks are assigned
   within geographically centralized clusters.  However, in
   geographically distributed heterogeneous networks, task placement
   decisions directly affect network traffic patterns, and traffic
   steering decisions affect network-wide compute utilization.

   Therefore, task deployment and traffic steering cannot be optimized
   independently.

   This document introduces a unified framework in which:

   *  Tasks select execution nodes from candidate compute-capable nodes.

   *  Task data flows consist of two stages:

      -  Input path: source node to execution node

      -  Output path: execution node to destination node

   *  Traffic may be split across multiple candidate paths.

   *  Both link bandwidth and multi-dimensional compute capacities are
      constrained.

   *  The objective is to minimize maximum compute resource utilization
      (and optionally network congestion).







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2.  Background and Architectural Motivation

2.1.  Heterogeneous Compute Networks

   Emerging computing networks contain:

   *  Forwarding-only nodes

   *  Compute-capable nodes

   *  Heterogeneous resource types (CPU, GPU, memory, etc.)

   These resources support distributed task execution rather than simple
   packet forwarding.

2.2.  Coupling Between Task Placement and Traffic Steering

   Selecting a compute node for a task determines:

   *  Input traffic injected into the network

   *  Output traffic delivered to the destination

   *  Compute resource load at the execution node

   Task placement therefore changes the traffic matrix, while steering
   decisions determine feasibility under bandwidth constraints.

   This coupling motivates joint optimization.

3.  Compute-Aware Task Routing Framework

3.1.  Design Principles

   *  Joint Optimization: Placement and steering are solved together.

   *  Two-Stage Flow Structure: Each task induces input and output
      flows.

   *  Multi-Dimensional Resource Awareness: Compute nodes have vector
      capacities.

   *  Load Balancing Objective: Minimize worst-case resource
      utilization.







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4.  High-Level Architecture

   ```text +--------------------------------------------------+ | Global
   Compute Coordination
   Plane | |--------------------------------------------------| | -
   Global resource abstraction (CPU/GPU/NPU) | | - Compute-aware traffic
   steering engine | | - Multi-domain policy & trust control |
   +---------------------------+----------------------+ | Capability
   Advertisement | +----------------------------------------------------
   --------------------------------+ | Wide-Area Compute Network
   (Compute DCN) | |----------------------------------------------------
   --------------------------------| |
   +--------------------------------+
   +--------------------------------+ | | | Regional Compute Center
   A | | Regional Compute Center B | | | |------------------------------
   --| |--------------------------------| | | | - Heterogeneous GPU
   cluster | | - Heterogeneous GPU cluster | | | | - Compute-intensive
   nodes | | - Memory-intensive nodes | | | | - Local scheduling
   agent | | - Local scheduling agent | | |
   +---------------+----------------+
   +---------------+----------------+ | | | | | | Source Node ->
   Execution Node Source Node -> Execution Node | | | | | | Execution
   Node -> Destination Node Execution Node -> Destination Node | |
   +---------------+----------------+
   +---------------+----------------+ | | | Edge / Access Layer | | Edge
   / Access Layer | | | |--------------------------------| |------------
   --------------------| | | | - Throughput-sensitive | | - Privacy-
   sensitive | | | | - Latency-sensitive | | - Cost-sensitive | | |
   +--------------------------------+
   +--------------------------------+ | +-------------------------------
   -----------------------------------------------------+ | End Users

   ```

5.  Network Model

   The network is represented as:

   G = (V, E)

   Where:

   *  V: set of nodes

   *  E: set of directed links

   *  M: compute-capable nodes, subset of V




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   *  R: forwarding-only nodes, subset of V

   *  Each link e has bandwidth capacity B[e]

   *  Each compute node m has d-dimensional capacity vector C[m]

6.  Task Model

   Each task i is defined by:

   *  Source node s_i

   *  Destination node d_i

   *  Compute demand vector w_i

   *  Input data size b_i[in]

   *  Output data size b_i[out].

   *  Candidate execution node set M_i

   Each task selects exactly one execution node from M_i.

7.  Resource Constraints

7.1.  Link Capacity Constraint

   For each link e:

   LinkLoad[e] is equal or less than B[e] * U[link][max]

   Where U[link][max] is a configurable utilization threshold.

7.2.  Compute Capacity Constraint

   For each compute node m and resource dimension k:

   Sum of assigned task demand in dimension k is equal or less than
   C[m][k] * U[node][max]

   Where U[node][max] is a configurable utilization threshold.

8.  Optimization Objective

   Minimize the maximum compute utilization across all compute nodes.

   This corresponds to min–max load balancing.



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   The framework may be extended to incorporate weighted trade-offs
   between compute utilization and link congestion.

9.  Applicability

   This framework applies to:

   *  AI inference distribution

   *  Edge-cloud collaboration

   *  Distributed accelerator networks

   *  Wide-area compute fabrics

   *  Compute-aware traffic engineering

   It is particularly relevant when:

   *  Compute resources are heterogeneous

   *  Tasks have multiple candidate execution locations

   *  Network bandwidth is constrained

   *  Load balancing is required

10.  Security Considerations

   Execution nodes process application data.  Placement decisions may
   depend on:

   *  Data locality requirements

   *  Administrative domain boundaries

   *  Trust relationships

   This document does not define security mechanisms.

11.  Terminology

   COMPUTE-CAPABLE NODE:
   A node that can execute tasks and provides multi-dimensional compute
   resources.

   FORWARDING-ONLY NODE:
   A node that forwards traffic but does not execute tasks.



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   TASK PLACEMENT:
   Selection of an execution node for a task.

   INPUT PATH:
   Route from task source to execution node.

   OUTPUT PATH:
   Route from execution node to destination.

   COMPUTE LOAD:
   Aggregate resource utilization at a compute node.

   LINK LOAD:
   Aggregate traffic load on a link.

   MIN-MAX LOAD BALANCING:
   Optimization objective minimizing worst-case resource utilization.

12.  Informative References

   [Elf]      "Elf: accelerate high-resolution mobile deep vision with
              content-aware parallel offloading", September 2021,
              <https://dl.acm.org/doi/abs/10.1145/3447993.3448628>.

   [RFC9556]  "Internet of Things (IoT) Edge Challenges and Functions",
              RFC 9556, n.d., <https://www.rfc-editor.org/rfc/rfc9556>.

Appendix A.  IANA Considerations

   This document has no IANA actions.

Appendix B.  References

B.1.  Normative References

   {::include normative}

B.2.  Informative References

   {::include informative}

Authors' Addresses

   Qing Li
   Pengcheng Laboratory
   Email: liq@pcl.ac.cn





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   Zeyu Luan
   Pengcheng Laboratory
   Email: luanzy@pcl.ac.cn


   Yong Jiang
   Tsinghua Shenzhen International Graduate School & Pengcheng Laboratory
   Email: jiangy@sz.tsinghua.edu.cn











































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