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Internet of Agents Protocol (IoA Protocol) for Heterogeneous Agent Collaboration
draft-yang-ioa-protocol-00

Document Type Active Internet-Draft (individual)
Authors Cheng Yang , Zhiyuan Liu , Aijun Wang
Last updated 2025-07-20
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draft-yang-ioa-protocol-00
Dispatch Working Group                                           C. Yang
Internet-Draft        Beijing University of Posts and Telecommunications
Intended status: Standards Track                                  Z. Liu
Expires: 21 January 2026                             Tsinghua University
                                                                 A. Wang
                                                           China Telecom
                                                            20 July 2025

   Internet of Agents Protocol (IoA Protocol) for Heterogeneous Agent
                             Collaboration
                       draft-yang-ioa-protocol-00

Abstract

   This draft defines a new agent collaboration protocol, named the
   Internet of Agents Protocol (IoA Protocol), to support distributed,
   heterogeneous agent collaboration in intelligent systems.  The IoA
   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 emerging 6G application scenarios
   such as intelligent transportation, smart healthcare, and large-scale
   human–AI teaming.

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
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   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 21 January 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   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  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   4
   3.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  IOA Methods . . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  IOA Architecture  . . . . . . . . . . . . . . . . . . . .   5
     4.2.  Heterogeneous Agent Integration . . . . . . . . . . . . .   6
     4.3.  Autonomous Team Formation . . . . . . . . . . . . . . . .   7
     4.4.  Session and Task Management Method  . . . . . . . . . . .   7
     4.5.  Message Protocol Overview . . . . . . . . . . . . . . . .   7
   5.  Relation to the A2A Protocol  . . . . . . . . . . . . . . . .   8
   6.  Future Enhancements for 6G-Enabled IoA Protocol . . . . . . .   9
     6.1.  Distributed Agent Registration and Discovery  . . . . . .  10
     6.2.  Positioning of the Protocol in the Network Layering
           System  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     6.3.  Enhanced Scalability and Fault Tolerance  . . . . . . . .  11
     6.4.  Semantic Interoperability and Ontology Alignment  . . . .  11
     6.5.  Security and Privacy Enhancements . . . . . . . . . . . .  11
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   9.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  12
   10. Normative References  . . . . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

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

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   In a typical collaborative setting shown in Figure 1, agents with
   specialized functions—including a Google Scholar Agent for academic
   search, an AI Research Specialist for conceptual planning, a PDF
   Agent for document analysis, and an Academic Writing Agent for
   content generation—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"                  |
|                                                                                       |
|        +----------------+        +----------------+        +----------------+         |
|        | AI Research    |<------>| Google Scholar |<------>| Academic       |         |
|        | Specialist     |        | Agent          |        | Writing Agent  |         |
|        | (Device A)     |        | (Device B)     |        | (Device C)     |         |
|        +----------------+        +----------------+        +----------------+         |
|                    \                     |                       /                    |
|                     \                    |                      /                     |
|                      \                   |                     /                      |
|                       \                  |                    /                       |
|                        \                 |                   /                        |
|                         \                |                  /                         |
|                          +---------------+-----------------+                          |
|                                          |                                            |
|                                 +----------------+                                    |
|                                 | PDF Agent      |                                    |
|                                 | (Device D)     |                                    |
|                                 +----------------+                                    |
|                                          |                                            |
|                             +------------+-------------+                              |
|                             |                          |                              |
|                             |       IoA Server         |                              |
|                             |                          |                              |
|                             +--------------------------+                              |
|                                                                                       |
+---------------------------------------------------------------------------------------+

                      Figure 1: Multi-agent collaboration scenario

   When the Google Scholar Agent encounters a specialized PDF parsing
   task beyond its capability, existing frameworks often fail to
   dynamically recruit the PDF Agent due to rigid team formation rules.
   Likewise, when the AI Research Specialist and Writing Agent attempt
   to synchronize intermediate results in real time, inflexible
   communication channels may result in delays or dropped information.

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   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 (IoA) 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.

   The design of the IoA Protocol aligns naturally with the vision of 6G
   networks, which aim to support ubiquitous intelligence through large-
   scale, low-latency, and semantic-driven communication.  By enabling
   agent collaboration across edge devices, mobile terminals, and cloud
   nodes, IoA complements 6G’s emphasis on edge-cloud-device
   coordination and distributed AI.  Its structured message design,
   dynamic team formation, and abstracted dialogue control offer the
   necessary protocol foundation to orchestrate intelligent services
   over future 6G infrastructures.

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] .

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3.  Terminology

   The following terms are defined in this draft:

   *  IoA: Internet of Agents, a protocol enabling distributed
      collaboration among heterogeneous agents across devices and 6G
      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, a application-layer protocol
      for distributed, collaborative, hypermedia information systems,
      referenced in IoA for interoperability with web-based agents,
      defined in [RFC9110]

   *  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 Protocol (IoA 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:

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   *  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 (e.g., Milvus) for semantic search   | |   - Agent Integration Block: Adapter for third-party agents                |
|   - Network Infra Block: WebSocket infrastructure                          | |   - Data Infra Block: Local DB (e.g., SQLite)                              |
|   - Security Block: Authentication and permission control                  | |   - Network Infra Block: WebSocket-based communication                     |
+----------------------------------------------------------------------------+ +----------------------------------------------------------------------------+

                  Figure 2: Layered architecture of IoA system

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.

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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 require ments;

   *  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 discus sion;

   *  Pause & trigger: The group chat is paused, waiting for the
      completion of specified asyn chronous 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.

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:

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

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

   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).

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

   The full structure of the message format is illustrated in Figure 3.

      +--------------------------+   +-----------------------------+
      |         Header           |   |  Autonomous Team Formation  |
      +--------------------------+   +-----------------------------+
      | sender: str              |   | goal: str                   |
      | state: enum              |   | team_members: list[str]     |
      | comm_id: str             |   | team_up_depth: int          |
      +--------------------------+   | max_turns: int              |
      +--------------------------+   +-----------------------------+
      |       Discussion         |   |      Task Assignment        |
      +--------------------------+   +-----------------------------+
      | content: str             |   | task_id: str                |
      | type: enum               |   | task_desc: str              |
      | next_speaker: list[str]  |   | task_conclusion: str        |
      +--------------------------+   | task_abstract: str          |
      +--------------------------+   +-----------------------------+
      |    Pause & Trigger       |
      +--------------------------+
      | triggers: list[str]      |
      +--------------------------+

             Figure 3: Structure of IoA Message Protocol

5.  Relation to the A2A Protocol

   The Agent-to-Agent (A2A) protocol is a communication standard
   designed to support standardized, secure, and modality-agnostic
   interaction between AI agents.  Built upon existing web technologies
   such as HTTP, Server-Sent Events (SSE), and JSON-RPC, A2A emphasizes
   default security, support for long-running tasks, and cross-modality
   interoperability.  It introduces the concept of an AgentCard to
   describe agent capabilities, enabling effective discovery and
   invocation.

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   The Internet of Agents (IoA) protocol shares the same fundamental
   goal with A2A: to break down communication barriers among agents and
   improve the overall efficiency of multi-agent systems.  Both
   protocols rely on network communication technologies and adopt
   similar approaches to message encoding, decoding, and task
   coordination.

   However, the two protocols diverge significantly in terms of design
   philosophy and core mechanisms:

   *  A2A focuses on enabling standardized communication through web-
      native technologies, effectively creating a "free trade zone" for
      agents where interoperability is built-in.  In contrast, the IoA
      protocol draws inspiration from Internet architecture and targets
      the problem of ecosystem fragmentation.  It establishes a system-
      level collaboration platform where heterogeneous agents can freely
      register, discover one another, and collaborate across platforms
      and devices.

   *  A2A is based on HTTP and JSON-RPC for communication, combined with
      task lifecycle management and capability discovery through
      AgentCard.  IoA, on the other hand, offers a more comprehensive
      collaboration framework, including agent registration, autonomous
      nested team formation, finite-state-machine-driven session
      control, and trigger-based task coordination.

   *  While A2A is suitable for standardized task responses and
      streaming updates, it lacks native support for dynamic session
      management and nested subtask structures.  IoA enables adaptive
      interaction flow via a session state machine, and its
      team_up_depth field supports recursive team formation and state
      transitions—making it more effective for handling complex and
      evolving task scenarios.

   In summary, A2A is well-suited for lightweight, standardized task
   interfaces, whereas IoA provides a more flexible and system-oriented
   protocol for large-scale, heterogeneous, and dynamic multi-agent
   collaboration.  The two protocols can complement each other at
   different layers, jointly advancing the development of agent
   communication technologies.

6.  Future Enhancements for 6G-Enabled IoA Protocol

   To fully realize the potential of 6G-enabled intelligent systems, the
   Internet of Agents (IoA) protocol requires continuous architectural
   evolution and standardization.  This section outlines key directions
   for future enhancements to improve scalability, decentralization,
   interoperability, and network integration.

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6.1.  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 6G-
   compatible 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 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.

6.2.  Positioning of the Protocol in the Network Layering System

   To achieve efficient integration of the IOA protocol with the 6G
   network protocol stack, the current design primarily positions IOA at
   the application layer, built on top of transport and session
   protocols such as TCP, UDP, WebSocket, and QUIC.  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.

   Since the IOA 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 6G-
   aware 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

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   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 6G scenarios; improving communication
   efficiency through semantic compression and 6G feature adaptation;
   and easily expanding to support new intelligent behaviors and 6G
   application scenarios through modular updates of the intelligence
   layer.

6.3.  Enhanced Scalability and Fault Tolerance

   To scale beyond millions of agents, the IoA 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 6G use cases.

6.4.  Semantic Interoperability and Ontology Alignment

   In highly heterogeneous environments, agents may describe their
   capabilities using different terminologies.  To address this, the IoA
   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.

6.5.  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.

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7.  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
   use TLS 1.3 as specified in [RFC8446] to ensure confidentiality and
   integrity, preventing eavesdropping or tampering.

8.  IANA Considerations

   [TBD] This document defines a new protocol for heterogeneous agent
   collaboration: the Internet of Agents (IoA) 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.

9.  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.

10.  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>.

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   [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>.

   [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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