Semantic-Driven Traffic Shaping Contract for AI Networks
draft-li-cats-aisemantic-contract-00

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

        Semantic-Driven Traffic Shaping Contract for AI Networks
                  draft-li-cats-aisemantic-contract-00

Abstract

   This document defines a "Semantic-Driven Shaping Contract".
   Traditional network protocols treat AI training and inference traffic
   as opaque byte streams, leading to highly inefficient scheduling.
   This contract allows applications or distributed training frameworks
   to explicitly pass "minimum necessary semantics" to the underlying
   network.  In exchange, the network commits to executing fine-grained,
   differentiated forwarding and resource allocation actions for tensor
   flows with diverse semantics, based on predefined rules and global
   real-time states.  This model significantly improves overall resource
   utilization and task completion times in heterogeneous computing
   networks, cross-domain intelligent computing centers, and integrated
   training-inference scenarios.

Status of This Memo

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   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.

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Table of Contents

   1.  Problem Statement: Limitations of Existing Network
           Mechanisms  . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Cross-Domain Amplification of Challenges  . . . . . . . . . .   3
   3.  The Semantic-Driven Mapping Loop: The Contract  . . . . . . .   3
     3.1.  Semantic Information Model (Metadata Model) . . . . . . .   3
     3.2.  Network Policy / Action Set . . . . . . . . . . . . . . .   4
   4.  Extended Use Case: Top-K Routing Semantics for MoE
           Architecture  . . . . . . . . . . . . . . . . . . . . . .   5
   5.  Deployment Considerations . . . . . . . . . . . . . . . . . .   5
     5.1.  Decision Location: Why In-Network?  . . . . . . . . . . .   5
     5.2.  RDMA / RoCEv2 Integration . . . . . . . . . . . . . . . .   5
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   5
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Problem Statement: Limitations of Existing Network Mechanisms

   In the era of large AI models, the "importance" of traffic
   dynamically shifts with the model's phase and exhibits a high degree
   of computability.  Existing traffic control and Quality of Service
   (QoS) mechanisms suffer from fundamental flaws in this context:

   *  *Coarse QoS Granularity and Invalid Implicit Assumptions:*
      Traditional QoS assumes that traffic within the same class has
      negligible variance and its importance remains stable at the
      session level.  However, in AI scenarios, QoS fails to
      differentiate between "early-layer activations" and "late-layer
      activations," nor can it distinguish between the "KV Cache of
      early tokens" and "tail tokens."

   *  *Static and Incomputable DiffServ Semantics:* Differentiated
      Services (DiffServ/DSCP) rely on static markings that the network
      blindly executes.  It cannot express dynamic computing semantics,
      such as "this flow is quantizable during congestion," "this flow
      tolerates a 5ms store-and-forward delay," or "this flow requires
      absolute preemption."

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   *  *Passive and Dimensionless ECN Feedback:* Existing Explicit
      Congestion Notification (ECN) mechanisms operate on the assumption
      that "end-systems know best how to respond to congestion" and that
      "rate reduction is the only correct response."  It possesses zero
      understanding of computing semantics, treating activations,
      gradients, blocking, and non-blocking operations equally.  In AI
      inference, the correct response to congestion is often "precision
      degradation (quantization)" or "prioritizing the draining of
      critical tensors," rather than blind rate reduction.

2.  Cross-Domain Amplification of Challenges

   In cross-domain intelligent computing networks characterized by
   multi-tasking, multi-tenancy, and integrated training and inference,
   the aforementioned flaws are severely amplified:

   *  *Time-scale Mismatch:* Cross-domain Round-Trip Times (RTT) reach
      the millisecond level, easily exceeding the "effective value
      window" of sensitive tensors like late-layer activations.  The
      network MUST make differentiation and routing decisions
      instantaneously during forwarding; post-facto congestion control
      feedback is entirely ineffective.

   *  *Resource & Path Asymmetry:* Cross-domain links are scarce, high-
      cost resources.  Delay-tolerant and compressible intermediate
      activations absolutely MUST NOT compete equally for cross-domain
      bandwidth with critical gradients that require immediate delivery.

   *  *Tight Compute-Network Coupling:* Traffic steering is no longer
      merely about "delivery to a fixed destination."  It requires
      dynamic selection based on compute heterogeneity (e.g., local GPU
      vs. remote FPGA).  A lack of semantic understanding leads to a
      severe mismatch between computing power and network resources.

3.  The Semantic-Driven Mapping Loop: The Contract

   The core of this draft is to establish a closed-loop mapping
   mechanism from "application-layer semantic input" to "network-side
   action commitment."

3.1.  Semantic Information Model (Metadata Model)

   The application layer MUST expose "exchangeable Semantic Metadata" to
   the network.  Based on the commonalities and specifics of training
   and inference tasks, this is categorized as follows:

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   *  *Traffic Class:* Explicitly identifies the data type (e.g.,
      Activation, Gradient, KV Cache, Parameter, Collaborative State
      Synchronization).

   *  *Urgency & Dependency:* Provides coarse-grained dependency hints
      (e.g., Early-token vs. Late-token) and the current layer or stage
      of the model (Layer ID / Pipeline Stage).

   *  *Tolerance & Sensitivity:*

      -  *Fidelity/Accuracy Sensitivity:* Indicates whether in-network
         low-precision quantization is permitted.

      -  *Loss/Latency Tolerance:* Indicates whether the flow permits
         buffering (store-and-forward) or dropping.

   *  *Compute Affinity:* Indicates the preferred characteristics of the
      underlying computing power (e.g., GPU, FPGA, CPU, or specific
      operator acceleration hardware).

3.2.  Network Policy / Action Set

   Upon receiving the aforementioned semantics, network nodes with
   global state awareness can execute a set of policies that transcend
   traditional routing:

   *  *Queueing / Scheduling:* Identifies flow states to guarantee
      absolute preemption for highly time-sensitive traffic.

   *  *Buffering / Store-and-forward:* Utilizes the storage resources of
      network devices to temporarily delay flows with high latency
      tolerance (e.g., large-block parameter pulls); it also implements
      cache multiplexing for inference requests from different users,
      directly optimizing hardware throughput without altering the model
      structure.

   *  *Shaping & In-network Quantization:* Triggers in-network low-
      precision quantization and sparsity strategies during congestion,
      rather than relying on simple packet dropping.

   *  *Steering:* Intelligently guides task flows to the most
      appropriate heterogeneous computing nodes based on Compute
      Affinity.

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4.  Extended Use Case: Top-K Routing Semantics for MoE Architecture

   For dynamic computing architectures like Mixture-of-Experts (MoE),
   this contract supports the definition of more complex routing
   metadata for intelligent scheduling in the network data plane:

   *  *Model Router Metadata:* Carries Token ID / Query vector
      summaries, Top-K candidate expert lists, weights/confidence
      levels, and positional markers (Token_pos).

   *  *System State Semantics:* Network nodes maintain real-time metrics
      for each expert node, including backlog queues, computing load,
      network latency, and bandwidth utilization.

   By matching these two semantics, the network can instantaneously
   determine which Expert node with the lightest load should receive the
   Token flow at the moment of forwarding.

5.  Deployment Considerations

5.1.  Decision Location: Why In-Network?

   Compared to edge devices (GPUs/NICs) that only possess local queuing
   information, in-network nodes (e.g., Core/Spine Switches) maintain a
   global perspective.  The network can perceive concurrent multi-tenant
   tasks and real-time multipath congestion states.  Crucially, it can
   make immediate decisions to buffer, slice, or reroute cross-domain
   traffic before it enters high-cost bottleneck links.

5.2.  RDMA / RoCEv2 Integration

   Intelligent computing centers rely heavily on RDMA.  The Semantic
   Header defined in this contract will be designed as Extension Headers
   for RoCEv2/UDP packets, or carried using specific reserved fields.
   This enables supporting hardware (such as the FPGA and parsing
   pipelines in the IntelliNode architecture) to extract metadata and
   execute policies at line rate (e.g., 400Gbps).

6.  Security Considerations

   To ensure the integrity of the Semantic-Driven Shaping Contract, the
   system MUST:

   *  *Authentication and Anti-Spoofing:* Prevent malicious tenants from
      tampering with the Urgency level or forging network states to
      unfairly preempt high-priority queues.

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7.  IANA Considerations

   This document requests that IANA allocate specific protocol numbers
   or RoCEv2 option type spaces for the AI Semantic Header to facilitate
   standardized deployment.

Authors' Addresses

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

   Teng gao
   Pengcheng Laboratory
   Email: gaot@pcl.ac.cn

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

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