A Framework for Compute-Aware Task Placement and Traffic Steering in Heterogeneous Computing Networks

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/.¶

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 2 September 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

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).¶

2. Background and Architectural Motivation

3. Compute-Aware Task Routing Framework

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¶

• 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

8. Optimization Objective

Minimize the maximum compute utilization across all compute nodes.¶

This corresponds to min–max load balancing.¶

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.¶

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

Authors' Addresses