Internet Research Task Force                                     C. Zhou
Internet-Draft                                                   H. Yang
Intended status: Informational                                   X. Duan
Expires: January 8, 2022                                    China Mobile
                                                                D. Lopez
                                                               A. Pastor
                                                          Telefonica I+D
                                                                   Q. Wu
                                                            M. Boucadair
                                                            C. Jacquenet
                                                            July 7, 2021

       Digital Twin Network: Concepts and Reference Architecture


   Digital Twin technology has been seen as a rapid adoption technology
   in Industry 4.0.  The application of Digital Twin technology in the
   networking field is meant to realize efficient and intelligent
   management and accelerate network innovation.  This document presents
   an overview of the concepts of Digital Twin Network (DTN), provides
   the definition and reference architecture, application scenarios, and
   then describes the benefits and key challenges of such technology.

Status of This Memo

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   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on January 8, 2022.

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Copyright Notice

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   Provisions Relating to IETF Documents
   ( in effect on the date of
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Requirements Language . . . . . . . . . . . . . . . . . . . .   3
   3.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   4
   4.  Definition of Digital Twin Network  . . . . . . . . . . . . .   4
   5.  Benefits of Digital Twin Network  . . . . . . . . . . . . . .   6
     5.1.  Lower the Cost of Network Optimization  . . . . . . . . .   7
     5.2.  Optimized Decision Making . . . . . . . . . . . . . . . .   7
     5.3.  Safer Assessment of Innovative Network Capabilities . . .   7
     5.4.  Privacy and Regulatory Compliance . . . . . . . . . . . .   8
     5.5.  Customize Network Operation Training  . . . . . . . . . .   8
   6.  Reference Architecture of Digital Twin Network  . . . . . . .   8
   7.  Challenges to build Digital Twin Network  . . . . . . . . . .  11
   8.  Interaction with IBN  . . . . . . . . . . . . . . . . . . . .  12
   9.  Application Scenarios . . . . . . . . . . . . . . . . . . . .  12
     9.1.  Human Training  . . . . . . . . . . . . . . . . . . . . .  12
     9.2.  ML Training . . . . . . . . . . . . . . . . . . . . . . .  13
     9.3.  DevOps-oriented certification . . . . . . . . . . . . . .  13
     9.4.  Network fuzzing . . . . . . . . . . . . . . . . . . . . .  13
   10. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .  14
   11. Security Considerations . . . . . . . . . . . . . . . . . . .  14
   12. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  14
   13. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   14. Open issues . . . . . . . . . . . . . . . . . . . . . . . . .  15
   15. References  . . . . . . . . . . . . . . . . . . . . . . . . .  15
     15.1.  Normative References . . . . . . . . . . . . . . . . . .  15
     15.2.  Informative References . . . . . . . . . . . . . . . . .  15
   Appendix A.  Change Logs  . . . . . . . . . . . . . . . . . . . .  15
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

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1.  Introduction

   With the advent of technologies such as 5G, Industrial Internet of
   Things, Edge Computing, and Artificial Intelligence, the ICT
   (Information and Communications Technology) and other vertical
   industries such as smart cities or smart manufacturers are
   transformed dramatically through replacing what is used to be manual
   processes with digital processes.

   With the fast growing of the network scale and the increased demand
   placed on the network, accommodating and adapting dynamically to
   customer needs becomes a big challenge to network operators.  Indeed,
   network operation and maintenance are becoming more complex due to
   higher complexity of the managed networks.  As such, providing
   innovations on network will be more and more difficult due to the
   high risk of interfering with existing services and higher trial cost
   if no reliable emulation platforms are available.

   Digital Twin is the real-time representation of physical entities in
   the digital world.  It has the characteristics of virtual-reality
   interrelation and real-time interaction, iterative operation and
   process optimization, as well as full life-cycle, and full business
   data-driven.  So far, it has been successfully applied in the fields
   of intelligent manufacturing, smart city, or complex system operation
   and maintenance [Tao2019] to help with not only object design and
   testing, but also operation and maintenance.

   A digital twin network platform can be built by applying Digital Twin
   technology to networks and creating a virtual image of physical
   network facilities (emulation).  Through the real-time data
   interaction between the physical network and its twin network, the
   digital twin network platform might help the network designers to
   achieve more simplification, automatic, resilient, and full life-
   cycle operation and maintenance.  Having an emulation platform that
   allows to reliably represent the state of a network is more reliable
   than a simulation platform.  The emulated platform can thus be used
   to assess specific behaviors before actual implementation in the
   physical network, tweak the network for better optimized behavior,
   run 'what-if' scenarios that can't be tested and evaluated easily in
   the physical network.  Service impact analysis tasks will also be

2.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP

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   14 [RFC2119][RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Definitions and Acronyms

   PLM: Product Lifecycle Management

   IBN: Intent-Based Networking

   AI: Artificial Intelligence

   ML: Machine Learning

   OAM: Operations, Administration, and Maintenance

   CI/CD: Continuous Integration / Continuous Delivery

4.  Definition of Digital Twin Network

   The concept of a virtual equivalent to a physical product or the
   digital twin was first introduced in the Product Lifecycle Management
   (PLM) course in 2003 by Scholar Michael Grieves [Grieves2014].  It
   has been widely acknowledged in both industry and academic
   publications.  However, there is no standard definition of "digital
   twin network" within the networking industry or SDOs.  This document
   defines digital twin network as a virtual representation of the
   physical network.  Such virtual representation of the network is
   meant to be used to analyze, diagnose, emulate, and then control the
   physical network based on data, model and interface.  To that aim, a
   real-time and interactive mapping is required between the physical
   network and its virtual twin network.

   As shown in Figure 1, the digital twin network involve four key
   technology elements: data, mapping, models, and interfaces

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               +-------------+                 +--------------+
               |             |                 |              |
               |  Mapping    |                 |  Interface   |
               |             |                 |              |
                        |                          |
                        |    Analyze, Diagnose     |
                        |                          |
                        | +----------------------+ |
                        | | NETWORK DIGITAL TWIN | |
                        | +----------------------+ |
            +------------+                        +------------+
            |            |   Emulate, Control     |            |
            |   Models   |                        |    Data    |
            |            |------------------------|            |
            +------------+                        +------------+

              Figure 1: Key Elements of Digital Twin Network

   Data:  A digital twin network should maintain historical data and/or
      real time data (configuration data, operational state data,
      topology data, trace data, metric data, process data, etc.) about
      its real-world twin (i.e., physical network) that are required by
      the models to represent and understand the states and behaviors of
      the real-world twin.  The data is characterized as the single
      source of the "truth" and populated in the data repository, which
      provides timely and accurate data service support for building
      various models..

   Models:  Techniques that involve collecting data from one or more
      sources in the real-world twin and developing a comprehensive
      representation of the data (e.g., system, entity, process) using
      specific models.  It is used as emulation and diagnosis basis to
      provides dynamics and elements on how live physical network
      operates and develop reasoning data utilized for decision-making.
      Various models such as service models, data models, dataset
      models, or knowledge graph can be used to represent the physical
      network assets and then instantiated to serve various network

   Interfaces:  Standardized interfaces can ensure the compatibility of
      digital twin network.  There are two major types of interface: (1)
      the interface between the digital twin network platform and the
      physical network infrastructure and (2) the interface between
      digital twin network platform and applications.  The former
      provides real time data collection and control on the physical
      network; the latter helps deliver application requirements to

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      digital twin network platform and exposure the various abilities
      to applications.

   Mapping:  Is used to identify the digital twin and the underlying
      entities and establish a real-time interactive mapping between the
      physical network and the twin network or between two twin
      networks.  The mapping can be:

      *  One to one (pairing, vertical): Synchronize between a physical
         network and its virtual twin network with continuous flow.

      *  One to many (coupling, horizontal): Synchronize among virtual
         twin networks with occasional data exchange.

      Such mapping provides a good visibility of actual status which
      makes it more convenient to analyze and understand what is going
      on in the physical network.  It also allows using the digital twin
      to optimize the performance and maintenance of the physical

   The digital twin network constructed based on the four core
   technology elements can analyze, diagnose, emulate, and control the
   physical network in the whole life cycle with the help of
   optimization algorithms, management methods, and expert knowledge.
   One of the objectives of such control is to master the digital twin
   network environment and its elements to derive the required system
   behavior, e.g., provide:

   o  repeatability: that is the capacity to replicate network
      conditions on-demand.

   o  reproducibility: i.e., the ability to replay successions of
      events, possibly under controlled variations.

5.  Benefits of Digital Twin Network

   Digital twin network can help enable closed-loop network management
   across the entire lifecycle, from deployment and emulation, to
   visualized assessment, physical deployment, and continuous
   verification.  In doing so, network operators (and end-users to some
   extent) can get a global, systemic, and consistent view of the
   network.  Also, network operators can safely exercise the enforcement
   of network planning policies, deployment procedures, etc., without
   jeopardizing the daily operation of the physical network.

   The benefits of digital twin network can be classified into: low cost
   of network optimization, optimized and safer decision-making, safer
   testing of innovative network capabilities (including "what if"

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   scenarios), privacy and regulatory compliance, and customize network
   operation training.  The following subsections further elaborate on
   such benefits.

5.1.  Lower the Cost of Network Optimization

   Large scale networks are complex to operate.  Since there is no
   effective platform for simulation, network optimization designs have
   to be tested on the physical network at the cost of jeopardizing its
   daily operation and possibly degrading the quality of the services
   supported by the network.  Such assessment greatly increases network
   operator's Operational Expenditure (OPEX) budgets too.

   With a digital twin network platform, network operators can safely
   emulate candidate optimization solutions before deploying them in the
   physical network.  In addition, the operator's OPEX on the real
   physical network deployment will be greatly decreased accordingly at
   the cost of the complexity of the assessment and the resources

5.2.  Optimized Decision Making

   Traditional network operation and management mainly focus on
   deploying and managing running services, but hardly support
   predictive maintenance techniques.

   Digital twin network can combine data acquisition, big data
   processing, and AI modeling to assess the status of the network, but
   also to predict future trends, and better organize predictive
   maintenance.  The ability to reproduce network behaviors under
   various conditions facilitates the corresponding assessment of the
   various evolution options as often as required.

5.3.  Safer Assessment of Innovative Network Capabilities

   Testing a new feature in an operational network is not only complex,
   it is also extremely risky.

   As mentioned above, digital twin network can greatly help assessing
   innovative network capabilities without jeopardizing the daily
   operation of the physical network.  In addition, it also helps
   researchers to explore network innovation (e.g., new network
   protocols, network AI/ML applications) efficiently, and network
   operators to deploy new technologies quickly with lower risks.  Take
   AI/ ML application as example, it is a conflict between the
   continuous high reliability requirement (i.e., 99.999%) of network
   and the slow learning speed or phase-in learning steps of AI/ML
   algorithms.  With digital twin network platform, AI/ML can complete

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   the learning and training with the sufficient data before deploying
   the model in the real network.  This will greatly encourage more
   network AI innovations in future networks.

5.4.  Privacy and Regulatory Compliance

   The requirements on data confidentiality and privacy on network
   providers increase the complexity of network management, as decisions
   made by computation logics such as an SDN controller may rely upon
   the payloads content.  As a result, the improvement of data-driven
   management requires complementary techniques that can provide a
   strict control based upon security mechanisms to guarantee data
   privacy protection and regulatory compliance.  Some examples of these
   techniques include payload inspection, including decryption with user
   explicit consents, or data anonymization mechanisms.

   Given digital twin network operation assumes the mapping between real
   traffic or services and the traffic used by the digital twin network
   for assessment purposes in particular, the need for privacy is of the
   utmost importance.  The lack of personal data permits to lower the
   privacy requirements and simplifies the use of privacy-preserving

5.5.  Customize Network Operation Training

   Network architectures can be complex, and their operation requires
   expert personnel.  Digital twin network offers an opportunity to
   train staff for customized networks and specific user needs.  Two
   salient examples are the application of new network architectures and
   protocols or the use of cyber-ranges to train security experts in the
   threat detection and mitigation.

6.  Reference Architecture of Digital Twin Network

   Based on the definition of the key digital twin network technology
   elements introduced in Section 4, a digital twin network architecture
   is depicted in Figure 2.  The digital twin network architecture is
   broken down into three layers: Application Layer, Network Digital
   Twin Layer and Physical Network Layer.

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        |   +-------+   +-------+          +-------+              |
        |   | App 1 |   | App 2 |   ...    | App n |   Application|
        |   +-------+   +-------+          +-------+              |
                      |Capability Exposure|intent input
                      |                   |
        |                                     Network Digital Twin|
        |  +--------+   +------------------------+   +--------+   |
        |  |        |   | Service Mapping Models |   |        |   |
        |  |        |   |  +------------------+  |   |        |   |
        |  | Data   +--->  |Functional Models |  +---> Digital|   |
        |  | Repo-  |   |  +-----+-----^------+  |   | Twin   |   |
        |  | sitory |   |        |     |         |   | Entity |   |
        |  |        |   |  +-----v-----+------+  |   |  Mgmt  |   |
        |  |        <---+  |  Basic Models    |  <---+        |   |
        |  |        |   |  +------------------+  |   |        |   |
        |  +--------+   +------------------------+   +--------+   |
                 |                            |
                 | data collection            | control
        |                   Physical Network                      |
        |                                                         |

         Figure 2: Reference Architecture of Digital Twin Network

   1.  The lowest layer is the Physical Network.  (All) network elements
       in the physical network exchange massive network data and control
       with network digital twin entity, through twin southbound
       interfaces.  As the physical object of the network twin, the
       physical network can be a mobile access network, a transport
       network, a mobile core, a backbone, etc.  The network can also be
       a data center network, a campus enterprise network, an industrial
       Internet of Things, etc.  The network can span across a single
       network domain or multiple network domains.

   2.  The Intermediate layer is the Network Digital Twin.  This layer
       includes three key subsystems: Data Repository subsystem, Service
       Mapping Models subsystem, and Digital Twin Entity Management

       *  Data Repository subsystem is responsible for collecting and
          storing various network data for building various models by
          collecting and updating the real-time operational data of
          various network elements through the twin southbound

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          interface, and providing data services (e.g., fast retrieval,
          concurrent conflict, batch service) and unified interfaces to
          Service Mapping Models subsystem.

       *  Service Mapping Models complete data modeling, provides data
          model instances for various network applications, and
          maximizes the agility and programmability of network services.
          The data models include two major types: basic and functional

          +  Basic models refer to the network element model and network
             topology model of the network digital twin based on the
             basic configuration, environment information, operational
             state, link topology and other information of the network
             element, to complete the real-time accurate
             characterization of the physical network.

          +  Functional models refer to various data models such as
             network analysis, simulation, diagnosis, prediction,
             assurance, etc.  The functional models can be constructed
             and expanded by multiple dimensions: by network type, there
             can be models serving for a single or multiple network
             domains; by function type, it can be divided into state
             monitoring, traffic analysis, security exercise, fault
             diagnosis, quality assurance and other models; by network
             lifecycle management, it can be divided into planning,
             construction, maintenance, optimization and operation. it
             can also be divided into general model and special-purpose
             model.  Specifically, multiple dimensions can be combined
             to create a data model for more specific application

       *  Digital Twin Entity Management completes the management
          function of digital twin network, records the life-cycle of
          the entity, visualizes and controls various elements of the
          network digital twin, including topology management, model
          management and security management.

   3.  Top layer is Application Layer.  Various applications (e.g., OAM,
       IBN) can effectively run over a digital twin network platform to
       implement either conventional or innovative network operations,
       with low cost and less service impact on real networks.  Network
       applications raise requirements that need to be addressed by the
       digital twin network.  Such requirements are exchanged through a
       northbound interface; then the service is emulated by various
       twin service instances.  Once checked, the changes can be safely
       deployed in the physical network.

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7.  Challenges to build Digital Twin Network

   As mentioned in the above section, digital twin networks can bring
   many benefits to network management as well as facilitate the
   introduction of innovative network capabilities.  However, building
   an effective and efficient digital twin network system remains a
   challenge.  The following is a list of the major challenges:

   o  Large scale challenge: The digital twin of large-scale networks
      will significantly increase the complexity of data acquisition and
      storage, the design and implementation of models.  And the
      requirements of software and hardware of the system will be even
      more constraining.

   o  Interoperability: It is difficult to establish a unified digital
      twin platform with a unified data model in the whole network
      domain due to the inconsistency of technical implementations and
      the heterogeneity of vendor technologies.

   o  Data modeling difficulties: Based on large-scale network data,
      data modeling should not only focus on ensuring the accuracy of
      model functions, but also need to consider the flexibility and
      scalability of the model.  Balancing these requirements further
      increase the complexity of building efficient and hierarchical
      functional data models.

   o  Real-time requirement: For services with real-time requirements,
      the processing of model simulation and verification through a
      digital twin network will increase the service delay, so the
      function and process of the data model need to be based on
      automated processing mechanism under various network application
      scenarios; at the same time, the real-time requirements will
      further increase performance requirements on the system software
      and hardware.

   o  Security risks: the digital twin network synchronizes all the data
      of physical networks in real time, which inevitably augments the
      attack surface, with a higher risk of information leakage, in

   To address these challenges, the digital twin network needs
   continuous optimization and breakthrough on key enabling technologies
   including data acquisition, data storage, data modeling, network
   visualization, interface standardization, and security assurance, so
   as to meet the requirements of compatibility, reliability, real-time
   and security.

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8.  Interaction with IBN

   Implementing Intent-Based Networking (IBN) is an innovative
   technology for life-cycle network management.  Future network will be
   possibly Intent-based, which means that users can input their
   abstract 'intent' to the network, instead of detailed policies or
   configurations on the network devices.
   [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the concept of
   "Intent" and provides an overview of IBN functionalities.  The key
   characteristic of an IBN system is that user's intent can be assured
   automatically via continuously adjusting the policies and validating
   the real-time situation.

   IBN can envisaged in a digital twin network context to show how
   digital twin network improves the efficiency of deploying network
   innovation.  To lower the impact on real networks, several rounds of
   adjustment and validation can be emulated on the digital twin network
   platform instead of directly on physical network.  Therefore, digital
   twin network can be an important enabler platform to implement IBN
   system and speed up the deployment of IBN in customer's network.

9.  Application Scenarios

   Digital twin network can be applied to solve different problems in
   network management and operation.

9.1.  Human Training

   The usual approach to network Operations, Administration, and
   Maintenance (OAM) with procedures applied by humans is open to errors
   in all these procedures, with impact in network availability and
   resilience.  Response procedures and actions for most relevant
   operational requests and incidents are commonly defined to reduce
   errors to a minimum.  The progressive automation of these procedures,
   such as predictive control or closed loop management, reduce the
   faults and response time, but still there is the need of a human-in-
   the-loop for multiples actions.  These processes are not intuitive
   and require training to learn how to respond.

   The use of digital twin network for this purpose in different network
   management activities will improve the operators performance.  One
   common example is cybersecurity incident handling, where cyber-range
   exercises are executed periodically to train security practitioners.
   Digital twin network will offer realistic environments, fitted to the
   real production networks.

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9.2.  ML Training

   Machine Learning requires data and their context to be available in
   order to apply it.  A common approach in the network management
   environment has been to simulate or import data in a specific
   environment (the ML developer lab), where they are used to train the
   selected model, while later, when the model is deployed in
   production, re-train or adjust to the production environment context.
   This demands a specific adaption period.

   Digital twin network simplifies the complete ML lifecycle development
   by providing a realistic environment, including network topologies,
   to generate the data required in a well-aligned context.  Dataset
   generated belongs to the digital twin network and not to the
   production network, allowing information access by third parties,
   without impacting data privacy.

9.3.  DevOps-oriented certification

   The potential application of CI/CD models network management
   operations increases the risk associated to deployment of non-
   validated updates, what conflicts with the goal of the certification
   requirements applied by network service providers.  A solution for
   addressing these certification requirements is to verify the specific
   impacts of updates on service assurance and SLAs using a digital twin
   network environment replicating the network particularities, as a
   previous step to production release.

   Digital twin network control functional block supports such dynamic
   mechanisms required by DevOps procedures.

9.4.  Network fuzzing

   Network management dependency on programmability increases systems
   complexity.  The behavior of new protocol stacks, API parameters, and
   interactions among complex software components are examples that
   imply higher risk to errors or vulnerabilities in software and

   Digital twin network allows to apply fuzzing testing techniques on a
   twin network environment, with interactions and conditions similar to
   the production network, permitting to identify and solve
   vulnerabilities, bugs and zero-days attacks before production

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10.  Summary

   Research on digital twin network has just started.  This document
   presents an overview of the digital twin network concepts.  Looking
   forward, further elaboration on digital twin network scenarios,
   requirements, architecture, and key enabling technologies should be
   promoted by the industry, so as to accelerate the implementation and
   deployment of digital twin network.

11.  Security Considerations

   This document describes concepts and definitions of digital twin
   network.  As such, the below security considerations remain high
   level, i.e., in the form of principles, guidelines or requirements.

   Security considerations of the digital twin network include:

   o  Secure the digital twin system itself.

   o  Data privacy protection.

   Securing the digital twin network system aims at making the digital
   twin system operationally secure by implementing security mechanisms
   and applying security best practices.  In the context of digital twin
   network, such mechanisms and practices may consist in data
   verification and model validation, mapping operations between
   physical network and digital counterpart network by authenticated and
   authorized users only.

   Synchronizing the data between the physical and the digital twin
   networks may increase the risk of sensitive data and information
   leakage.  Strict control and security mechanisms must be provided and
   enabled to prevent data leaks.

12.  Acknowledgements

   Diego Lopez and Antonio Pastor were partly supported by the European
   Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
   and grant agreement no. 871808 (INSPIRE-5Gplus).

13.  IANA Considerations

   This document has no requests to IANA.

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14.  Open issues

   o  Investigate related digital twin network work and identify the
      differences and commonality, e.g., How is this concept and
      architecture different from digital twin for industry application?
      How can existing network management models be re-used?

15.  References

15.1.  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,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

15.2.  Informative References

              Grieves, M., "Digital twin: Manufacturing excellence
              through virtual factory replication", 2003.

              Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", draft-irtf-nmrg-ibn-concepts-definitions-03
              (work in progress), February 2021.

   [Tao2019]  Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in
              Industry: State-of-the-Art. IEEE Transactions on
              Industrial Informatics, vol. 15, no. 4.", April 2019.

Appendix A.  Change Logs

   v03 - v04

   o  Change the I-D title from "Concepts of Digital Twin Network" to
      "Digital Twin Network: Concepts and Reference Architecture".

   o  Update data definition and models definitions to clarify their

   o  Remove the orchestration element and consolidated into control
      functionality building block in the digital twin network.

Zhou, et al.             Expires January 8, 2022               [Page 15]

Internet-Draft        Digital Twin Network Concept             July 2021

   o  Clarify the mapping relation (one to one, and one to many) in the
      mapping definition.

   o  Add explanation text for continuous verification.

   v02 - v03

   o  Split interaction with IBN part as a separate section.

   o  Fill security section;

   o  Clarify the motivation in the introduction section;

   o  Use new boilerplate for requirements language section;

   o  Key elements definition update.

   o  Other editorial changes.

   o  Add open issues section.

   o  Add section on application scenarios.

Authors' Addresses

   Cheng Zhou
   China Mobile
   Beijing  100053


   Hongwei Yang
   China Mobile
   Beijing  100053


   Xiaodong Duan
   China Mobile
   Beijing  100053


Zhou, et al.             Expires January 8, 2022               [Page 16]

Internet-Draft        Digital Twin Network Concept             July 2021

   Diego Lopez
   Telefonica I+D


   Antonio Pastor
   Telefonica I+D


   Qin Wu
   101 Software Avenue, Yuhua District
   Nanjing, Jiangsu  210012


   Mohamed Boucadair
   Rennes 35000


   Christian Jacquenet
   Rennes 35000


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