



Dispatch Working Group                                           C. Yang
Internet-Draft        Beijing University of Posts and Telecommunications
Intended status: Standards Track                                  Z. Liu
Expires: 23 October 2026                             Tsinghua University
                                                                 A. Wang
                                                           China Telecom
                                                           21 April 2026


 Internet of Agents Task Protocol (IoA Task Protocol) for Heterogeneous
                          Agent Collaboration
                  draft-yang-dmsc-ioa-task-protocol-03

Abstract

   This draft defines a new agent collaboration protocol, named the
   Internet of Agents Task Protocol (IoA Task Protocol), to support
   distributed, heterogeneous agent collaboration in intelligent
   systems.  The IoA Task Protocol enables dynamic team formation,
   adaptive task coordination, and structured communication among agents
   with diverse architectures, tools, and knowledge sources.  Through a
   layered architecture and extensible message format, it supports
   decentralized deployment across devices and can interoperate with
   existing frameworks.  The protocol is particularly suited to large-
   scale intelligent collaboration scenarios—such as intelligent
   transportation, smart healthcare, and large-scale human–AI
   teaming—across heterogeneous network environments, including fixed
   networks, edge–cloud infrastructures, and emerging mobile networks
   such as 6G.

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 23 October 2026.





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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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   extracted from this document must include Revised BSD License text as
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   5
   3.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  IoA Methods . . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.1.  IoA Architecture  . . . . . . . . . . . . . . . . . . . .   6
     4.2.  Heterogeneous Agent Integration . . . . . . . . . . . . .   7
     4.3.  Autonomous Team Formation . . . . . . . . . . . . . . . .   7
     4.4.  Session and Task Management Method  . . . . . . . . . . .   7
     4.5.  Message Protocol Overview . . . . . . . . . . . . . . . .   8
   5.  Positioning of the IoA Task Protocol in the Network Layering
           System  . . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  Relation to the A2A Protocol  . . . . . . . . . . . . . . . .  10
   7.  Future Enhancements across Heterogeneous Networks . . . . . .  11
     7.1.  Distributed Agent Registration and Discovery  . . . . . .  11
     7.2.  Enhanced Scalability and Fault Tolerance  . . . . . . . .  12
     7.3.  Semantic Interoperability and Ontology Alignment  . . . .  12
     7.4.  Security and Privacy Enhancements . . . . . . . . . . . .  12
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   10. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  13
   11. Normative References  . . . . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   With the rapid advancement of large language models (LLMs) and
   multimodal autonomous agents, modern intelligent systems are
   increasingly constructed as collaborative networks of multiple
   agents.  These agents are expected to work together to solve complex,
   open-ended tasks.  However, they often differ in capabilities, tools,
   runtime environments, and communication patterns, leading to
   significant challenges in interoperability, dynamic coordination, and



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   cross-device deployment.  As a result, current multi-agent frameworks
   fall short of the flexibility and generality required in real-world
   applications.

   In a typical collaborative setting shown in Figure 1, agents with
   specialized functions—including on-device AI Agents on Device A for
   conceptual planning, Device B for academic search, Device C for
   content generation, and Device D for document analysis—must work
   together to complete a research paper on “Internet of Agents.” These
   agents are distributed across devices (e.g., laptops, edge nodes,
   cloud services), and each relies on different execution frameworks or
   data formats.

+---------------------------------------------------------------------------------------+
|                                                                                       |
|                 Task: Write a research paper on "Internet of Agents"                  |
|                                                                                       |
|        +-----------------+        +-----------------+        +-----------------+      |
|        | On-Device AI    |<------>| On-Device AI    |<------>| On-Device AI    |      |
|        | Agent (Device A)|        | Agent (Device B)|        | Agent (Device C)|      |
|        +-----------------+        +-----------------+        +-----------------+      |
|                    \                     |                       /                    |
|                     \                    |                      /                     |
|                      \                   |                     /                      |
|                       \                  |                    /                       |
|                        \                 |                   /                        |
|                         \                |                  /                         |
|                          +---------------+-----------------+                          |
|                                          |                                            |
|                                 +-----------------+                                   |
|                                 | On-Device AI    |                                   |
|                                 | Agent (Device D)|                                   |
|                                 +-----------------+                                   |
|                                          |                                            |
|                             +------------+-------------+                              |
|                             |                          |                              |
|                             |       IoA Server         |                              |
|                             |                          |                              |
|                             +--------------------------+                              |
|                                                                                       |
+---------------------------------------------------------------------------------------+
                      Figure 1: Multi-agent collaboration scenario









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   When Device B encounters a specialized PDF parsing task beyond its
   capability, existing frameworks often fail to dynamically recruit
   Device D due to rigid team formation rules.  Likewise, when Device A
   and Device C attempt to synchronize intermediate results in real
   time, inflexible communication channels may result in delays or
   dropped information.

   Existing solutions exhibit several key limitations:

   *  Closed frameworks that restrict integration with third-party
      agents such as AutoGPT or Open Interpreter;

   *  Single-device simulation that fails to reflect cross-device
      deployment scenarios typical in edge-cloud collaboration;

   *  Hard-coded workflows that prevent agents from switching between
      synchronous and asynchronous task execution at runtime.

   To address these challenges, this draft introduces the Internet of
   Agents Task Protocol (IoA Task Protocol)—a layered, extensible
   collaboration standard designed for intelligent multi-agent systems.
   The core goal of the protocol is to enable seamless collaboration
   among heterogeneous agents across devices, tools, and execution
   environments.  It supports:

   *  Agent integration via a standardized interface and registration
      mechanism;

   *  Dynamic team formation across distributed environments;

   *  Finite-state machine-based session control for flexible and
      autonomous dialogue management;

   *  Structured message formats with group routing, task assignment,
      and response coordination.
















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   The design of the IoA Task Protocol aligns naturally with the
   evolution of intelligent networked systems, including fixed networks
   and next-generation mobile networks such as 6G, which aim to support
   ubiquitous intelligence through large-scale, low-latency, and
   semantic-driven communication.  By enabling agent collaboration
   across fixed-network infrastructures, edge devices, mobile terminals,
   and cloud nodes, IoA supports coordinated intelligence across
   heterogeneous network environments, including both fixed networks and
   mobile networks such as 6G.  Its structured message design, dynamic
   team formation, and abstracted dialogue control provide a
   foundational protocol framework for orchestrating intelligent
   services across heterogeneous network infrastructures, including
   fixed networks and future mobile networks such as 6G.

2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119] .

3.  Terminology

   The following terms are defined in this draft:

   *  IoA: Internet of Agents, an architecture enabling distributed
      collaboration among heterogeneous agents across devices and
      networks, defined in Section 4

   *  Agent Registry Block: A server-side module storing structured
      capability descriptions of all registered agents, supporting
      semantic search for team formation, defined in Section 4

   *  Team Formation Block: A client-side module responsible for
      initiating, joining, or disbanding agent teams based on task
      requirements, including nested sub-teams, defined in Section 4

   *  Session State Machine: A finite-state model governing
      collaboration states (Discussion, Synchronous Task Assignment,
      Asynchronous Task Assignment, Pause and Trigger, Conclusion) for
      adaptive dialogue management, defined in Section 4

   *  HTTP: Hypertext Transfer Protocol, an application-layer protocol
      for distributed, collaborative, hypermedia information systems,
      referenced in IoA for interoperability with web-based agents,
      defined in [RFC9110]






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   *  JSON-RPC: A remote procedure call protocol encoded in JSON,
      referenced in IoA for structured communication between web-based
      agents, defined in [RFC8259]

   *  QUIC: A transport layer protocol providing secure, low-latency
      communication over UDP, used in IoA for real-time agent messaging,
      defined in [RFC9000]

4.  IoA Methods

4.1.  IoA Architecture

   The Internet of Agents Task Protocol (IoA Task Protocol) enables
   distributed collaboration among heterogeneous agents through a
   layered architecture and distributed communication protocol.  It
   supports seamless integration across devices, toolchains, and runtime
   environments.

   The IoA system adopts a three-layer architecture implemented
   symmetrically at both the server and client side:

   *  Server-side: Handles global coordination, agent discovery, group
      management, and message routing.

   *  Client-side: Encapsulates individual agents and provides
      interfaces for team collaboration and local task execution.

   An overview of the layered structure is shown in Figure 2.

+----------------------------------------------------------------------------+ +----------------------------------------------------------------------------+
|                                   Server                                   | |                                   Client                                   |
|----------------------------------------------------------------------------| |----------------------------------------------------------------------------|
| Interaction Layer:                                                         | | Interaction Layer:                                                         |
|   - Agent Query Block: Handles semantic agent search queries               | |   - Team Formation Block: Forms/join teams for assigned goals              |
|   - Group Setup Block: Manages group/team creation                         | |   - Communication Block: Handles chat messaging and event updates          |
|   - Message Routing Block: Routes messages within chat groups              | |----------------------------------------------------------------------------|
|----------------------------------------------------------------------------| | Data Layer:                                                                |
| Data Layer:                                                                | |   - Agent Contact Block: Caches past collaborators                         |
|   - Agent Registry Block: Stores capability descriptions of all agents     | |   - Group Info Block: Stores task metadata and group state                 |
|   - Session Management Block: Tracks WebSocket sessions and group states   | |   - Task Management Block: Tracks subtasks, assignment, and progress       |
|----------------------------------------------------------------------------| |----------------------------------------------------------------------------|
| Foundation Layer:                                                          | | Foundation Layer:                                                          |
|   - Data Infra Block: Vector database for semantic search                  | |   - Agent Integration Block: Adapter for third-party agents                |
|   - Network Infra Block: WebSocket infrastructure                          | |   - Data Infra Block: Local DB                                             |
|   - Security Block: Authentication and permission control                  | |   - Network Infra Block: WebSocket-based communication                     |
+----------------------------------------------------------------------------+ +----------------------------------------------------------------------------+

                  Figure 2: Layered architecture of IoA system



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4.2.  Heterogeneous Agent Integration

   IoA supports the integration of heterogeneous agents from diverse
   sources through a unified interface, including third-party agents
   such as AutoGPT, Open Interpreter, and embodied robotic agents.

   When a new agent joins the IoA, its client wrapper undergoes a
   registration process with the server.  During this registration, the
   agent is expected to provide a comprehensive description of its
   capabilities, skills, and domains of expertise.  For an agent c_i,
   its description is denoted as d_i, and is stored in the Agent
   Registry Block within the Data Layer of the server.

   The set of all registered agents is denoted as C = {c₁, c₂, ..., cₙ},
   where each c_i is associated with its capability description d_i.
   This mechanism enables future semantic matching and intelligent task
   allocation.

4.3.  Autonomous Team Formation

   Agents initiate the search process by submitting capability
   requirements to the Agent Query Block.  The server performs semantic
   matching using vector similarity and returns candidate agents from
   the Agent Registry Block.

   IoA supports nested team structures.  An initial group is formed for
   the main goal, and subgroups are recursively created if subtasks
   require new capabilities.  This forms a hierarchical tree structure,
   reducing communication complexity and organizational overhead.

   The entire team formation process is autonomous, task-driven, device-
   agnostic, and self-organizing.

4.4.  Session and Task Management Method

   IoA models group conversations and collaboration using a finite-state
   machine with five abstract states:

   *  Discussion: Agents engage in general dialogue, exchange ideas, and
      clarify task requirements;

   *  Synchronous task assignment: Tasks are assigned to specific
      agents, pausing the group chat until completion;

   *  Asynchronous task assignment: Tasks are assigned without
      interrupting the ongoing discussion;





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   *  Pause & Trigger: The group chat is paused, waiting for the
      completion of specified asynchronous tasks;

   *  Conclusion: Marks the end of the collaboration, prompting a final
      summary.

   State transitions are managed autonomously by a coordinator agent
   using the conversation history and session context to determine the
   next state and speaker.  Only the coordinator agent is allowed to
   modify the session state, concurrent transition proposals are
   resolved by the coordinator using timestamps.

4.5.  Message Protocol Overview

   The agent message protocol in IoA is designed for extensibility and
   flexibility, enabling effective collaboration among heterogeneous
   agents.  Each message consists of two main parts: a header and a
   payload.

   The header contains essential metadata to ensure proper routing and
   processing.  Key fields include:

   *  sender: The unique identifier of the agent sending the message.

   *  state: The current collaboration state associated with the
      message.

   *  group_id: The identifier of the group chat to which the message
      belongs.

   The common header fields shared by all message types are illustrated
   in Figure 3.

       +--------------------------+
       |         Header           |
       +--------------------------+
       | sender: str              |
       | state: enum              |
       | group_id: str            |
       +--------------------------+
              Figure 3: Common header fields in IoA Message Protocol

   The payload carries the main content of the message and varies
   depending on message type.  Common fields include:

   *  message_type: Indicates the purpose of the message (e.g.,
      discussion, task assignment, pause and trigger).




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   *  next_speaker: The identifier(s) of the agent(s) expected to
      respond.

   The full structure of the message format is illustrated in Figure 4.

     +-----------------------------+   +-----------------------------+
     |  Autonomous Team Formation  |   |      Task Assignment        |
     +-----------------------------+   +-----------------------------+
     | goal: str                   |   | task_id: str                |
     | team_members: list[str]     |   | task_desc: str              |
     | team_up_depth: int          |   | task_conclusion: str        |
     | max_turns: int              |   | task_abstract: str          |
     +-----------------------------+   +-----------------------------+
     +-----------------------------+   +-----------------------------+
     |         Discussion          |   |    Pause & Trigger          |
     +-----------------------------+   +-----------------------------+
     | content: str                |   | triggers: list[str]         |
     | type: enum                  |   +-----------------------------+
     | next_speaker: list[str]     |
     +-----------------------------+
            Figure 4: Structure of IoA Message Protocol

5.  Positioning of the IoA Task Protocol in the Network Layering System

   From an architectural perspective, the IoA Task Protocol is
   positioned at the application layer, built on top of transport and
   session protocols such as TCP, UDP, WebSocket, and QUIC.  This
   positioning allows IoA to remain independent of underlying network
   technologies and enables deployment across heterogeneous networking
   environments, including fixed networks, edge–cloud infrastructures,
   and mobile networks.

   From the perspective of functional mapping, the corresponding
   relationship between IoA's three-layer architecture and the computer
   network layers is as follows:

   *  Interaction Layer → Maps to the application layer, responsible for
      high-level logic such as message protocols, group collaboration,
      and session state transitions.

   *  Data Layer → Spans the application layer and session layer,
      managing agent states, group metadata, and context tracking.

   *  Foundation Layer → Corresponds to the transport layer and system
      infrastructure, including secure communication channels (e.g.,
      WebSocket/QUIC), databases, and network service modules.





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   Since the IoA Task Protocol involves intelligent behaviors such as
   agent orchestration, semantic-driven interaction, and session
   control, an intelligence layer can be introduced above the
   traditional application layer.  This layer encapsulates core
   intelligent collaboration logic—such as semantic-based agent
   matching, AI-driven session strategy optimization, dynamic task
   decomposition, and team reorganization—into standardized message
   formats.  This layer shields upper-layer applications and lower-layer
   protocols from the complexity of intelligent decision-making,
   enabling them to focus on their core functions without concerning
   themselves with the details of how intelligence is implemented (e.g.,
   scenario-specific task execution at the application layer, reliable
   data transmission at the transport layer).  Its advantages are
   reflected in: standardizing the collaboration of heterogeneous
   agents, reducing integration costs across diverse deployment
   environments; improving communication efficiency through semantic
   compression and adaptive feature optimization; and enabling modular
   extensibility to support new intelligent behaviors and emerging
   application scenarios.

6.  Relation to the A2A Protocol

   The IoA Task Protocol is related to the Agent2Agent (A2A) protocol in
   that both aim to improve interoperability among heterogeneous agents
   across different systems and deployment environments.  Both protocols
   rely on structured network communication and support interactions
   that may extend beyond a single request response exchange.  However,
   the two protocols differ in design focus, especially in how they
   model and handle tasks.

   Compared with A2A, the main distinctions of the IoA Task Protocol are
   as follows:

   *  In A2A, a Task is a stateful unit of work processed by an A2A
      server for an A2A client.  A2A separates Message from Artifact,
      and treats task handling primarily as a remote execution contract
      with explicit lifecycle progression and result return.  A
      completed task is expected to return generated outputs through
      artifacts.  By contrast, in the IoA Task Protocol, task handling
      is embedded into a broader multi-agent collaboration process.
      Task related fields such as task_id, task_desc, task_conclusion,
      and task_abstract are integrated with collaboration-oriented
      fields such as goal, team_members, team_up_depth, next_speaker,
      and triggers.  As a result, a task in A2A is mainly an execution
      object, while a task in the IoA Task Protocol is also a
      coordination primitive for collaborative orchestration.





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   *  A2A mainly standardizes interaction between an A2A client and an
      A2A server, including message exchange, task lifecycle management,
      and output delivery.  The IoA Task Protocol, in contrast, targets
      multi-agent collaboration scenarios, where tasks may be assigned,
      discussed, paused, resumed, and further decomposed across multiple
      agents within a session.

   *  A2A provides standardized support for task status tracking,
      streaming, and push-based notification.  The IoA Task Protocol
      further defines coordination mechanisms for collaborative
      execution, including synchronous task assignment, asynchronous
      task assignment, pause and trigger control, and nested team
      formation.

   *  A2A is primarily an interoperability protocol for remote agent
      invocation and task execution.  The IoA Task Protocol places
      greater emphasis on session-driven orchestration for dynamic,
      team-based, and evolving multi-agent workflows.

   In summary, A2A and the IoA Task Protocol are related but distinct.
   A2A focuses on standardized remote task execution and lifecycle
   managed result delivery, whereas the IoA Task Protocol focuses on
   task handling within a collaborative multi-agent session.  The key
   difference is therefore not whether tasks are supported, but how
   tasks are treated: A2A treats tasks primarily as execution units,
   while the IoA Task Protocol treats tasks as both execution units and
   coordination units in collaborative workflows.

7.  Future Enhancements across Heterogeneous Networks

   To fully realize the potential of intelligent systems operating
   across heterogeneous network environments—including fixed networks
   and next-generation mobile networks such as 6G—the Internet of Agents
   Task Protocol (IoA Task Protocol) requires continuous architectural
   evolution and standardization.  This section outlines key directions
   for future enhancements to improve scalability, decentralization,
   interoperability, and network integration.

7.1.  Distributed Agent Registration and Discovery

   The current IoA design relies on a centralized server model, which
   may limit scalability and introduce single points of failure under
   large-scale deployment.  A promising direction is to adopt a
   decentralized registration and discovery mechanism, where agents can
   publish their capabilities to a shared registry accessible via a
   network-accessible web-based interface.  Inspired by Domain Name
   System (DNS) and search engines, agents could be discoverable through
   keyword-based or semantic search at scale, enabling lightweight



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   browser-based or API-based discovery across domains.

   This decentralized lookup layer would allow IoA to support scenarios
   where agents operate across multiple domains, owners, and physical
   networks, while still maintaining secure and authenticated
   interaction through digital signatures and trust mechanisms.

7.2.  Enhanced Scalability and Fault Tolerance

   To scale beyond millions of agents, the IoA Task Protocol should
   adopt sharding and region-based message routing.  Distributed
   registries and dynamic load balancing can reduce latency and avoid
   bottlenecks.  Caching of frequent agent metadata at edge nodes is
   also critical for fast retrieval in latency-sensitive deployment
   scenarios.

7.3.  Semantic Interoperability and Ontology Alignment

   In highly heterogeneous environments, agents may describe their
   capabilities using different terminologies.  To address this, the IoA
   Task Protocol should support ontology mapping and alignment
   mechanisms.  This allows agents with differing skill descriptors to
   still interoperate, using shared or translated task definitions
   during team formation and dialogue.

7.4.  Security and Privacy Enhancements

   For mission-critical 6G scenarios (e.g., autonomous vehicles, medical
   AI), the protocol must incorporate stronger security primitives.
   This includes:

   *  End-to-end encryption with forward secrecy.

   *  Support for zero-trust architectures with agent attestation and
      secure enclaves.

   *  Fine-grained access control based on agent role and session
      context.

8.  Security Considerations

   IoA servers and agents store sensitive data including capability
   descriptors, session state metadata, and task execution logs, which
   consume memory and computational resources.  To mitigate risks of
   resource exhaustion and unauthorized access, [RFC6749] (OAuth 2.0)
   mandates that IoA entities must authenticate peers via token-based
   validation before processing registration requests or collaboration
   messages.  Additionally, all data transmission between entities must



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   use TLS 1.3 as specified in [RFC8446] to ensure confidentiality and
   integrity, preventing eavesdropping or tampering.

9.  IANA Considerations

   [TBD] This document defines a new protocol for heterogeneous agent
   collaboration: the Internet of Agents Task Protocol (IoA Task
   Protocol).  The protocol's code point allocation will be determined
   in subsequent revisions as the standard matures, in accordance with
   IANA's relevant registration procedures.

10.  Acknowledgement

   Thanks Weize Chen, Ziming You, Ran Li, Yitong Guan, Chen Qian,
   Chenyang Zhao, Ruobing Xie, Maosong Sun and Yu Hao for their valuable
   comments on this draft.

11.  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>.

   [RFC6749]  Hardt, D., Ed., "The OAuth 2.0 Authorization Framework",
              RFC 6749, DOI 10.17487/RFC6749, October 2012,
              <https://www.rfc-editor.org/info/rfc6749>.

   [RFC7432]  Sajassi, A., Ed., Aggarwal, R., Bitar, N., Isaac, A.,
              Uttaro, J., Drake, J., and W. Henderickx, "BGP MPLS-Based
              Ethernet VPN", RFC 7432, DOI 10.17487/RFC7432, February
              2015, <https://www.rfc-editor.org/info/rfc7432>.

   [RFC8259]  Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
              Interchange Format", STD 90, RFC 8259,
              DOI 10.17487/RFC8259, December 2017,
              <https://www.rfc-editor.org/info/rfc8259>.

   [RFC8446]  Rescorla, E., "The Transport Layer Security (TLS) Protocol
              Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
              <https://www.rfc-editor.org/info/rfc8446>.

   [RFC9000]  Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
              Multiplexed and Secure Transport", RFC 9000,
              DOI 10.17487/RFC9000, May 2021,
              <https://www.rfc-editor.org/info/rfc9000>.





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   [RFC9110]  Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
              Ed., "HTTP Semantics", STD 97, RFC 9110,
              DOI 10.17487/RFC9110, June 2022,
              <https://www.rfc-editor.org/info/rfc9110>.

Authors' Addresses

   Cheng Yang
   Beijing University of Posts and Telecommunications
   10 Xitucheng Road, Haidian District
   Beijing
   Beijing, 100876
   China
   Email: yangcheng@bupt.edu.cn


   Zhiyuan Liu
   Tsinghua University
   30 Shuangqing Road, Haidian District
   Beijing
   Beijing, 100084
   China
   Email: liuzy@tsinghua.edu.cn


   Aijun Wang
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   Beijing, 102209
   China
   Email: wangaj3@chinatelecom.cn



















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