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Digital Twin Network: Concepts and Reference Architecture

Document Type Active Internet-Draft (nmrg RG)
Authors Cheng Zhou , Hongwei Yang , Xiaodong Duan , Diego Lopez , Antonio Pastor , Qin Wu , Mohamed Boucadair , Christian Jacquenet
Last updated 2022-10-24
Replaces draft-zhou-nmrg-digitaltwin-network-concepts
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Internet Research Task Force                                     C. Zhou
Internet-Draft                                                   H. Yang
Intended status: Informational                                   X. Duan
Expires: 27 April 2023                                      China Mobile
                                                                D. Lopez
                                                               A. Pastor
                                                          Telefonica I+D
                                                                   Q. Wu
                                                            M. Boucadair
                                                            C. Jacquenet
                                                         24 October 2022

       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 develop various rich network
   applications and realize efficient and cost effective data driven
   network management and accelerate network innovation.

   This document presents an overview of the concepts of Digital Twin
   Network, provides the basic definitions and a reference architecture,
   lists a set of application scenarios, and discusses 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 27 April 2023.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Acronyms & Abbreviations  . . . . . . . . . . . . . . . .   4
     2.2.  Definitions . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Introduction and Concepts of Digital Twin Network . . . . . .   4
     3.1.  Background of Digital Twin  . . . . . . . . . . . . . . .   4
     3.2.  Digital Twin for Networks . . . . . . . . . . . . . . . .   5
     3.3.  Definition of Digital Twin Network  . . . . . . . . . . .   6
   4.  Benefits of Digital Twin Network  . . . . . . . . . . . . . .   9
     4.1.  Optimized Network Total Cost of Operation . . . . . . . .  10
     4.2.  Optimized Decision Making . . . . . . . . . . . . . . . .  10
     4.3.  Safer Assessment of Innovative Network Capabilities . . .  10
     4.4.  Privacy and Regulatory Compliance . . . . . . . . . . . .  11
     4.5.  Customized Network Operation Training . . . . . . . . . .  11
   5.  Challenges to Build Digital Twin Network  . . . . . . . . . .  11
   6.  A Reference Architecture of Digital Twin Network  . . . . . .  13
   7.  Enabling Technologies to Build Digital Twin Network . . . . .  16
     7.1.  Data Collection and Data Services . . . . . . . . . . . .  16
     7.2.  Network Modeling  . . . . . . . . . . . . . . . . . . . .  17
     7.3.  Network Visualization . . . . . . . . . . . . . . . . . .  18
     7.4.  Interfaces  . . . . . . . . . . . . . . . . . . . . . . .  19
   8.  Interaction with IBN  . . . . . . . . . . . . . . . . . . . .  19
   9.  Sample Application Scenarios  . . . . . . . . . . . . . . . .  20
     9.1.  Human Training  . . . . . . . . . . . . . . . . . . . . .  20
     9.2.  Machine Learning Training . . . . . . . . . . . . . . . .  20
     9.3.  DevOps-Oriented Certification . . . . . . . . . . . . . .  21
     9.4.  Network Fuzzing . . . . . . . . . . . . . . . . . . . . .  21
     9.5.  Digital Asset Management  . . . . . . . . . . . . . . . .  21
   10. Research Perspectives: A Summary  . . . . . . . . . . . . . .  22
   11. Security Considerations . . . . . . . . . . . . . . . . . . .  22
   12. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  23
   13. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  23

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   14. Open issues . . . . . . . . . . . . . . . . . . . . . . . . .  23
   15. References  . . . . . . . . . . . . . . . . . . . . . . . . .  23
     15.1.  Normative References . . . . . . . . . . . . . . . . . .  23
     15.2.  Informative References . . . . . . . . . . . . . . . . .  24
   Appendix A.  Change Logs  . . . . . . . . . . . . . . . . . . . .  28
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  29

1.  Introduction

   The fast growth of network scale and the increased demand placed on
   these networks require them to accommodate and adapt dynamically to
   customer needs, implying a significant challenge to network
   operators.  Indeed, network operation and maintenance are becoming
   more complex due to higher complexity of the managed networks and the
   sophisticated services they are delivering.  As such, providing
   innovations on network technologies, management and operation will be
   more and more challenging due to the high risk of interfering with
   existing services and the higher trial costs if no reliable emulation
   platforms are available.

   A Digital Twin is the real-time representation of a physical entity
   in the digital world.  It has the characteristics of virtual-reality
   interrelation and real-time interaction, iterative operation and
   process optimization, full life-cycle and comprehensive data-driven
   network infrastructure.  Currently, digital twin has been widely
   acknowledged in academic publications.  See more in Section 3.

   A digital twin for networks platform can be built by applying Digital
   Twin technologies to networks and creating a virtual image of
   physical network facilities (called herein, emulation).  Basically,
   the digital twin for networks is an expansion platform of network
   simulation.  The main difference compared to traditional network
   management systems is the interactive virtual-real mapping and data
   driven approach to build closed-loop network automation.  Therefore,
   a digital twin network platform is more than an emulation platform or
   network simulator.

   Through the real-time data interaction between the physical network
   and its twin network(s), the digital twin network platform might help
   the network designers to achieve more simplification, automatic,
   resilient, and full life-cycle operation and maintenance.  More
   specifically, the digital twin network can, thus, be used to develop
   various rich network applications and assess specific behaviors
   (including network transformation) before actual implementation in
   the physical network, tweak the network for better optimized
   behavior, run 'what-if' scenarios that cannot be tested and evaluated
   easily in the physical network.  In addition, service impact analysis
   tasks can also be facilitated.

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2.  Terminology

2.1.  Acronyms & Abbreviations

   IBN:  Intent-Based Networking

   AI  Artificial Intelligence

   CI/CD:  Continuous Integration/Continuous Delivery

   ML:  Machine Learning

   OAM:  Operations, Administration, and Maintenance

   PLM:  Product Lifecycle Management

2.2.  Definitions

   This document makes use of the following terms:

   Digital Twin:  a virtual instance of a physical system (twin) that is
      continually updated with the latter's performance, maintenance,
      and health status data throughout the physical system's life

   Digital twin network:  a digital twin that is used in the context of
      networking.  This is also called, digital twin for networks.  See
      more in Section 3.3.

3.  Introduction and Concepts of Digital Twin Network

3.1.  Background of Digital Twin

   The concept of the "twin" dates to the National Aeronautics and Space
   Administration (NASA) Apollo program in the 1970s, where a replica of
   space vehicles on Earth was built to mirror the condition of the
   equipment during the mission [Rosen2015].

   In 2003, Digital Twin was attributed to John Vickers by Michael
   Grieves in his product lifecycle management (PLM) course as "virtual
   digital representation equivalent to physical products"
   [Grieves2014].  Digital twin can be defined as a virtual instance of
   a physical system (twin) that is continually updated with the
   latter's performance, maintenance, and health status data throughout
   the physical system's life cycle [Madni2019].  By providing a living
   copy of physical system, digital twins bring numerous advantages,
   such as accelerated business processes, enhanced productivity, and
   faster innovation with reduced costs.  So far, digital twin has been

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   successfully applied in the fields of intelligent manufacturing,
   smart city, or complex system operation and maintenance to help with
   not only object design and testing, but also management aspects

   Compared with 'digital model' and 'digital shadow', the key
   difference of 'digital twin' is the direction of data between the
   physical and virtual systems [Fuller2020].  Typically, when using a
   digital twin, the (twin) system is generated and then synchronized
   using data flows in both directions between physical and digital
   components, so that control data can be sent, and changes between the
   physical and digital objectives of systems are automatically
   represented.  This behavior is unlike a 'digital model' or 'digital
   shadow', which are usually synchronized manually, lacking of control
   data, and might not have a full cycle of data integrated.

   At present (2022), there is no unified definition of digital twin
   framework.  The industry, scientific research institutions, and
   standards developing organizations are trying to define a general or
   domain-specific framework of digital twin.  [Natis-Gartner2017]
   proposed that building a digital twin of a physical entity requires
   four key elements: model, data, monitoring, and uniqueness.
   [Tao2019] proposed a five-dimensional framework of digital twin {PE,
   VE, SS, DD, CN}, in which PE represents physical entity, VE
   represents virtual entity, SS represents service, DD represents twin
   data, and CN represents the connection between various components.
   [ISO-2021] issued a draft standard for digital twin manufacturing
   system, and proposed a reference framework including data collection
   domain, device control domain, digital twin domain, and user domain.

3.2.  Digital Twin for Networks

   Communication networks provide a solid foundation for implementing
   various 'digital twin' applications.  At the same time, in the face
   of increasing business types, scale and complexity, a network itself
   also needs to use digital twin technology to seek enhanced and
   optimized solutions compared to relying solely on the physical
   network.  The motivation for digital twin network can somehow be
   traced back to some earlier concepts, such as "shadow MIB", inductive
   modeling techniques, parallel systems, etc.  Since 2017, the
   application of digital twin technology in the field of communication
   networks has gradually been researched as illustrated by the (non-
   exhaustive) list of examples that are listed hereafter.

   Within academia, [Dong2019] established the digital twin of 5G mobile
   edge computing (MEC) network, used the twin offline to train the
   resource allocation optimization and normalized energy-saving
   algorithm based on reinforcement learning, and then updated the

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   scheme to MEC network.  [Dai2020] established a digital twin edge
   network for mobile edge computing system, in which a twin edge server
   is used to evaluate the state of entity server, and the twin mobile
   edge computing system provides data for training offloading strategy.
   [Nguyen2021] discusses how to deploy a digital twin for complex 5G
   networks.  [Hong2021] presents a digital twin platform towards
   automatic and intelligent management for data center networks, and
   then proposes a simplified the workflows of network service
   management.  In addition, international workshops dedicated to
   digital twin in networking field have already appeared, such as IEEE
   DTPI 2021 - Digital Twin Network Online Session [DTPI2021], and IEEE
   NOMS 2022 - TNT workshop [TNT2022].

   Although the application of digital twin technology in networking has
   started, the research of digital twin for networks technology is
   still in its infancy.  Current applications focus on specific
   scenarios (such as network optimization), where network digital twin
   is just used as a network simulation tool to solve the problem of
   network operation and maintenance.  Combined with the characteristics
   of digital twin technology and its application in other industries,
   this document believes that digital twin network can be regarded as
   an indispensable part of the overall network system and provides a
   general architecture involving the whole life cycle of physical
   network in the future, serving the application of network innovative
   technologies such as network planning, construction, maintenance and
   optimization, improving the automation and intelligence level of the

3.3.  Definition of Digital Twin Network

   So far, there is no standard definition of "digital twin network"
   within the networking industry.  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, models, and interfaces.  To that aim, a real-time and
   interactive mapping is required between the physical network and its
   virtual twin network.

   Referring the characteristics of digital twin in other industries and
   the characteristics of the networking itself, the digital twin
   network should involve four key elements: data, mapping, models and
   interfaces as shown in Figure 1.

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               +-------------+                 +--------------+
               |             |                 |              |
               |  Mapping    |                 |  Interface   |
               |             |                 |              |
                        |                          |
                        |    Analyze, Diagnose     |
                        |                          |
                        | +----------------------+ |
                        | | Digital Twin Network | |
                        | +----------------------+ |
            +------------+                        +------------+
            |            |   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 "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.  These models are used as emulation and diagnosis
      basis to provide dynamics and elements on how the live physical
      network operates and generates reasoning data utilized for

      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 interoperability
      of digital twin network.  There are two major types of interfaces:

      *  The interface between the digital twin network platform and the
         physical network infrastructure.

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      *  The interface between digital twin network platform and

      The former provides real-time data collection and control on the
      physical network.  The latter helps in delivering application
      requests to the digital twin network platform and exposing the
      various platform capabilities to applications.

   Mapping:  Used to identify the digital twin and the underlying
      entities and establish a real-time interactive relation 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 flows.

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

      Such mappings provide a good visibility of actual status, making
      the digital twin suitable 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 its 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:

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

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

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   Note: Real-time interaction is not always mandatory for all twins.
   When testing some configuration changes or trying some innovative
   techniques, the digital twins can behave as a simulation platform
   without the need of real time telemetry data.  And even in this
   scenario, it is better to have interactive mapping capability so that
   the validated changes can be tested in real network whenever required
   by the testers.  In most other cases (e.g., network optimization,
   network fault recovery), real-time interaction between virtual and
   real network is mandatory.  This way, digital twin network can help
   achieve the goal of autonomous network or self-driven network.

4.  Benefits of Digital Twin Network

   Digital twin network can help enabling closed-loop network management
   across the entire lifecycle, from deployment and emulation, to
   visualized assessment, physical deployment, and continuous
   verification.  By doing so, network operators and end-users to some
   extent, as allowed by specific application interfaces, can maintain a
   global, systemic, and consistent view of the network.  Also, network
   operators and/or enterprise user can safely exercise the enforcement
   of network planning policies, deployment procedures, etc., without
   jeopardizing the daily operation of the physical network.

   The main difference between digital twin network and simulation
   platform is the use of interactive virtual-real mapping to build
   closed-loop network automation.  Simulation platforms are the
   predecessor of the digital twin network, one example of such a
   simulation platform is network simulator [NS-3], which can be seen as
   a variant of digital twin network but with low fidelity and lacking
   for interactive interfaces to the real network.  Compared with those
   classical approaches, key benefits of digital twin network can be
   summarized as follows:

   1)  Using real-time data to establish high fidelity twins, the
       effectiveness of network simulation is higher; then the
       simulation cost will be relatively low.

   2)  The impact and risk on running networks is low when automatically
       applying configuration/policy changes after the full analysis and
       required verifications (e.g., service impact analysis) within the
       twin network.

   3)  The faults of the physical network can be automatically captured
       by analyzing real-time data, then the correction strategy can be
       distributed to the physical network elements after conducting
       adequate analysis within the twins to complete the closed-loop
       automatic fault repair.

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   The following subsections further elaborate such benefits in details.

4.1.  Optimized Network Total Cost of Operation

   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, 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 involved.

4.2.  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.

4.3.  Safer Assessment of Innovative Network Capabilities

   Testing a new feature in an operational network is not only complex,
   but also extremely risky.  Service impact analysis is required to be
   adequately achieved prior to effective activation of a new feature.

   Digital twin network can greatly help assessing innovative network
   capabilities without jeopardizing the daily operation of the physical
   network.  In addition, it 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%) and the slow learning speed or phase-in learning steps of
   AI/ML algorithms.  With digital twin network, AI/ML can complete the
   learning and training with the sufficient data before deploying the
   model in the real network.  This would encourage more network AI
   innovations in future networks.

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4.4.  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 packet payloads.  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.  This may range from
   flow identification (using the archetypal five-tuple of addresses,
   ports and protocol) to techniques requiring some degree of payload
   inspection, all of them considered suitable to be associated to an
   individual person, and hence requiring strong protection and/or data
   anonymization mechanisms.

   With strong modeling capability provided by the digital twin network,
   very limited real data (if at all) will be needed to achieve similar
   or even higher level of data-driven intelligent analysis.  This way,
   a lower demand of sensitive data will permit to satisfy privacy
   requirements and simplify the use of privacy-preserving techniques
   for data-driven operation.

4.5.  Customized 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
   threat detection and mitigation.

5.  Challenges to Build Digital Twin Network

   According to [Hu2021], the main challenges in building and
   maintaining digital twins can be summarized as the following five

   *  Data acquisition and processing

   *  High-fidelity modeling

   *  Real-time, two-way communication between the virtual and the real

   *  Unified development platform and tools

   *  Environmental coupling technologies

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   Compared with other industrial fields, digital twin in networking
   field has its unique characteristics.  On one hand, network elements
   and system have higher level of digitalization, which implies that
   data acquisition and virtual-real communication are relatively easy
   to achieve.  On the other hand, there are various different type of
   network elements and typologies in the network field; and the network
   size is characterized by the numbers of nodes and links in it but the
   network size growth pace can not meet the service needs, especially
   in the deployment of end to end service which spans across multiple
   administrative domains.  So, the construction of a digital twin
   network system needs to consider the following major challenges:

   Large scale challenge:  A digital twin of large-scale networks will
      significantly increase the complexity of data acquisition and
      storage, the design and implementation of relevant models.  The
      requirements of software and hardware of the digital twin network
      system will be even more constraining.  Therefore, efficient and
      low cost tools in various fields should be required.  Take data as
      an example, massive network data can help achieve more accurate
      models.  However, the cost of virtual-real communication and data
      storage becomes extremely expensive, especially in the multi-
      domain data-driven network management case, therefore efficient
      tools on data collection and data compression methods must be

   Interoperability:  Due to the inconsistency of technical
      implementations and the heterogeneity of vendor adopted
      technologies, it is difficult to establish a unified digital twin
      network system with a common technology in a network domain.
      Therefore, it is needed firstly to propose a unified architecture
      of digital twin network, in which all components and
      functionalities are clear to all stakeholders; then define
      standardized and unified interfaces to connect all network twins
      via ensuring necessary compatibility.

   Data modeling difficulties:  Based on large-scale network data, data
      modeling should not only focus on ensuring the accuracy of model
      functions, but also has to consider the flexibility and
      scalability to compose and extend as required to support large
      scale and multi-purpose applications.  Balancing these
      requirements further increases the complexity of building
      efficient and hierarchical functional data models.  As an optional
      solution, straightforwardly clone the real network using
      virtualized resources is feasible to build the twin network when
      the network scale is relatively small.  However, it will be of
      unaffordable resource cost for larger scales network.  In this
      case, network modeling using mathematical abstraction or
      leveraging the AI algorithms will be more suitable solutions.

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   Real-time requirements:  Network services normally have real-time
      requirements, the processing of model simulation and verification
      through a digital twin network will introduce the service latency.
      Meanwhile, the real-time requirements will further impose
      performance requirements on the system software and hardware.
      However, given the nature of distributed systems and propagation
      delays, it is challenge to keep network digital twins in sync or
      auto-sync between physical network and digital twin network.
      Changes to the digital object automatically drive changes in the
      physical object can be even challenging.  To address these
      requirements, the function and process of the data model need to
      be based on automated processing mechanism under various network
      application scenarios.  On the one hand, it is needed to design a
      simplified process to reduce the time cost for tasks in network
      twin as much as possible; on the other hand, it is recommended to
      define the real-time requirements of different applications, and
      then match the corresponding computing resources and suitable
      solutions as needed to complete the task processing in the twin.

   Security risks:  A digital twin network has to synchronize all or
      subset of the data related to involved physical networks in real
      time, which inevitably augments the attack surface, with a higher
      risk of information leakage, in particular.  On one hand, it is
      mandatory to design more secure data mechanism leveraging legacy
      data protection methods, as well as innovative technologies such
      as block chain.  On the other hand, the system design can limit
      the data (especially raw data) requirement on building digital
      twin network, leveraging innovative modeling technologies such as
      federal learning.

   In brief, to address the above listed challenges, it is important to
   firstly propose a unified architecture of digital twin network, which
   defines the main functional components and interfaces (Section 6).
   Then, relying upon such an architecture, it is required to continue
   researching on the key enabling technologies including data
   acquisition, data storage, data modeling, interface standardization,
   and security assurance.

6.  A Reference Architecture of Digital Twin Network

   Based on the definition of the key digital twin network technology
   elements introduced in Section 3.3, a digital twin network
   architecture is depicted in Figure 2.  This digital twin network
   architecture is broken down into three layers: Application Layer,
   Digital Twin Layer, and Physical Network Layer.

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

          Figure 2: Reference Architecture of Digital Twin Network

   Physical Network:  All or subset of network elements in the physical
      network exchange network data and control messages with a network
      digital twin instance, through twin-physical control interfaces.
      The physical network can be a mobile access network, a transport
      network, a mobile core, a backbone, etc.  The physical network can
      also be a data center network, a campus enterprise network, an
      industrial Internet of Things, etc.

      The physical network can span across a single network
      administrative domain or multiple network administrative domains.

      This document focuses on the IETF related physical network such as
      IP bearer network and data center network.

   Digital Twin Layer:  This layer includes three key subsystems: Data
      Repository subsystem, Service Mapping Models subsystem, and
      Digital Twin Network Management subsystem.  These key subsystems
      can be placed in one single network administrative domain and
      provide the service to the application (e.g.,SDN controller) in

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      other network administrative domain, or lied in every network
      administrative domain and coordinate between each other to provide
      services to the application in the upper layer.

      One or multiple digital twin network instances can be built and

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

      *  Service Mapping Models complete data modeling, provide 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 models.

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

         -  Functional models refer to various data models used for
            network analysis, emulation, 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.
            Functional models can also be divided into general models
            and special-purpose models.  Specifically, multiple
            dimensions can be combined to create a data model for more
            specific application scenarios.

            New applications might need new functional models that do
            not exist yet.  If a new model is needed, 'Service Mapping
            Models' subsystem will be triggered to help creating new
            models based on data retrieved from 'Data Repository'.

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      *  Digital Twin Network Management fulfils the management function
         of digital twin network, records the life-cycle transactions of
         the twin entity, monitors the performance and resource
         consumption of the twin entity or even of individual models,
         visualizes and controls various elements of the network digital
         twin, including topology management, model management and
         security management.

      Notes: 'Data collection' and 'change control' are regarded as
      southbound interfaces between virtual and physical network.  From
      implementation perspective, they can optionally form a sub-layer
      or sub-system to provide common functionalities of data collection
      and change control, enabled by a specific infrastructure
      supporting bi-directional flows and facilitating data aggregation,
      action translation, pre-processing and ontologies.

   Application Layer:  Various applications (e.g., Operations,
      Administration, and Maintenance (OAM)) 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 make requests that
      need to be addressed by the digital twin network.  Such requests
      are exchanged through a northbound interface, so they are applied
      by service emulation at the appropriate twin instance(s).

7.  Enabling Technologies to Build Digital Twin Network

   This section briefly describes several key enabling technologies to
   build digital twin work system, based on the challenges and the
   reference architecture described in above sections.  Actually, each
   enabling technology is worth of deep researching respectively and

7.1.  Data Collection and Data Services

   Data collection technology is the foundation of building data
   repository for digital twin network.  Target driven mode should be
   adopted for data collection from heterogeneous data sources.  The
   type, frequency and method of data collection shall meet the
   application of digital twin network.  Whenever building network
   models for a specific network application, the required data can be
   efficiently obtained from the data repository.

   Diverse existing tools and methods (e.g., SNMP, NETCONF [RFC6241],
   IPFIX [RFC7011], telemetry [RFC9232]) can be used to collect
   different type of network data.  YANG data models and associated
   mechanisms defined in [RFC8639][RFC8641] enable subscriber-specific
   subscriptions to a publisher's event streams.  Such mechanisms can be

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   used by subscriber applications to request for a continuous and
   customized stream of updates from a YANG datastore.  Moreover, some
   innovative methods (e.g., sketch-based measurement) can be used to
   acquire more complex network data, such as network performance data.
   Furthermore, data transformation and aggregation capabilities can be
   used to improve the applicability on network modelling.  Toward
   building data repository for a digital twin system, data collection
   tools and methods should be as lightweight as possible, so as to
   reduce the volume of required network equipment resources, and
   meaningful so it can be useful.  Several solutions related to data
   collection are work-in-progress in IETF/IRTF, e.g., adaptive
   subscription [I-D.ietf-netconf-adaptive-subscription], efficient data
   collection [I-D.zcz-nmrg-digitaltwin-data-collection], and contextual
   information [I-D.claise-opsawg-collected-data-manifest].

   Data repository works to effectively store large-scale and
   heterogeneous network data, as well provide data and services to
   build various network models.  So, it is also necessary to study
   technologies regarding data services including fast search, batch-
   data handling, conflict avoidance, data access interfaces, etc.

7.2.  Network Modeling

   The basic network element models and topology models help generate
   virtual twin of the network according to the network element
   configuration, operation data, network topology relationship, link
   state and other network information.  Then the operation status can
   be monitored and displayed, and the network configuration change and
   optimization strategy can be pre-verified.

   For small scale network, network simulating tools (e.g., [NS-3],
   [Mininet], etc.) and emulating tools (e.g., [EVE-NG], [GNS-3]) can be
   used to build basic network models.  By using the packet processing
   capability of virtual network element, such tools can quickly verify
   the functions of the control plane and data plane.  However, this
   modeling method also has many limitations, including high resource
   consumption, poor performance analysis ability, and poor scalability.
   For large scale network, mathematical abstraction methods can be used
   to build basic network models efficiently.  Knowledge graph, network
   calculus, and formal verification can be candidate methods.  Some
   relevant researches have emerged in recent years, such as [Hong2021],
   [G2-SIGCOMM], and [DNA-2022].  Going forward, how to improve the
   extensibility and accuracy of the models is still a big challenge.

   As an example, the theory of bottleneck structures introduced in
   [G2-SIGCOMM, G2-SIGMETRICS] can be used to construct a mathematical
   model of the network (see also
   [I-D.giraltyellamraju-alto-bsg-requirements] for more info).  A

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   bottleneck structure is a computational graph that efficiently
   captures the topology, the routing and flow properties of the
   network.  The graph embeds the latent relationships that exist
   between bottlenecks and the application flows in a distributed
   system, providing an efficient mathematical framework to compute the
   ripple effects of perturbations (e.g., a flow arriving or departing
   from the system, or the dynamic change in capacity of a wireless
   link, among others).  Because these perturbations can be seen as
   mathematical derivatives of the communication system, bottleneck
   structures can be used to compute optimized network configurations,
   providing a natural engineering sandbox for building network models.
   One of the key advantages of bottleneck structures is that they can
   be used to compute (symbolically or numerically) key performance
   indicators of the network (e.g., expected flow throughput, projected
   flow completion time, etc.) without the need to use computationally
   intensive simulators.  This capability can be especially useful when
   building a digital twin or a large-scale network, potentially saving
   orders or magnitude in computational resources in comparison to
   simulation or emulation-based approaches.

   The functional model aims to realize the dynamic evolution of network
   performance evaluation and intelligent decision-making.  Data driven
   AI/ML algorithm will play a great role in building complex network
   functional models.  As a research hotspot in recent years, many
   successfully cases have been demonstrated, such as [RouteNet],
   [MimicNet], etc.  In the future, in addition to improving the
   generalization ability and interpretability of AI models, we also
   need to focus on how to improve the real-time and interactivity of
   model reasoning based on data and control in network digital twin

7.3.  Network Visualization

   It is the internal requirement of the digital twin network system to
   use network visibility technology to visually present the data and
   model in the network twin with high fidelity and intuitively reflect
   the interactive mapping between the physical network entity and the
   network twin. Network Visibility technology can help users understand
   the internal structure of the network, and also help mine valuable
   information hidden in the network.

   Network Visibility can use algorithms such as hierarchical layout,
   heuristic layout or force oriented layout (or a combination of
   several algorithms) for topology layout.  And the related topology
   data can be acquired using solutions provided in [RFC8345],
   [RFC8346], [RFC8944], etc.  Meanwhile, digital twin network system
   can select different interaction methods or combinations of
   interaction methods to realize the visual dynamic interaction mapping

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   of virtual and real networks.  The data query technology, such as
   SPARQL, can be used to express queries across diverse data sources,
   whether the data is stored natively as RDF or viewed as RDF via

7.4.  Interfaces

   Based on the reference architecture, there are three types of
   interfaces on building a digital twin network system.

   1)  Network-facing interfaces are twin interfaces between the
       physical network and its twin entity.  They are responsible for
       information exchange between physical network and network digital
       twin.  The candidate interfaces can be SNMP, NETCONF, etc.

   2)  Application-facing interfaces are Application-facing interfaces
       between the network digital twin and applications.  They are
       responsible for information exchange between network digital twin
       and network applications.  The lightweight and extensible
       [RESTFul] interface can be the candidate northbound interface.

   3)  Internal interfaces are within network digital twin layer.  They
       are responsible for information exchange between the three
       subsystems: Data Repository, Service Mapping Models, and Digital
       Twin Network Management.  These interfaces should be of high-
       speed, high-efficiency and high-concurrency.  The candidate
       interfaces or protocols can be XMPP (defined in [RFC7622]), and
       HTTP/3.0 (defined in [RFC9114]).

   All interfaces are recommended to be open and standardized so as to
   help avoid either hardware or software vendor lock, and achieve
   interoperability.  Besides the interfaces list above, some new
   interfaces or protocols can be created to better serve digital twin
   network system.

8.  Interaction with IBN

   Implementing Intent-Based Networking (IBN) is an innovative
   technology for life-cycle network management.  Future networks 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.  [RFC9315] clarifies the
   concept of "Intent" and provides an overview of IBN functionalities.
   The key characteristic of an IBN system is that user intent can be
   assured automatically via continuously adjusting the policies and
   validating the real-time situation.

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   IBN can be 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, the
   digital twin network can be an important enabler platform to
   implement IBN systems and fooster their deployment.

9.  Sample 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 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.

9.2.  Machine Learning 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.

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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 Service Level Agreements
   (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

9.5.  Digital Asset Management

   With the development of enterprise digitization, the number of
   enterprise Internet of Objects (IoT) devices, virtualized Cloud
   assets (e.g., virtual firewall), and network asset (e.g., switches,
   routers) also increases.  The endpoints connected to an enterprise
   network lack coherent modelling and lifecycle management because
   different services are modelled, collected, processed, and stored
   separately.  The same category of network devices (including network
   endpoints) may be repeatedly discovered, processed, and stored.
   Therefore, the inventory is difficult to manage when they are tracked
   in different places without formal synchronization procedures.

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   Digital twin management can be used as a means to ensure consistent
   representation and reporting of asset types.  In doing so, the
   enforcement of security policies and assessment will be further
   simplified.  Such an approach will ease implementing a unified
   control strategy for all assets types connected to an enterprise
   network.  It also make actors on assets more accountable for
   breaching their compliance promises.  Special care should be
   considerd to protext the inventory data since it may be gather
   privacy-senstive information.

   The digital asset management for twins can be used, for example, to
   exercise the implication of End of Life (EoL), dependency, and
   hardware dependency "what-if" scenarios.

10.  Research Perspectives: A Summary

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

11.  Security Considerations

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

   Security considerations of the digital twin network include:

   *  Secure the digital twin system itself.

   *  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.

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12.  Acknowledgements

   Many thanks to the NMRG participants for their comments and reviews.
   Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia, Jerome
   Francois, Jordi Paillisse, Luis Miguel Contreras Murillo, Alexander
   Clemm, Qiao Xiang, Ramin Sadre, Pedro Martinez-Julia, Wei Wang,
   Zongpeng Du, and Peng Liu.

   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.

14.  Open issues

   *  Some technologies (e.g.  Network connectivity, Real-time data
      communication, Collaboration management, conflict detection and
      resolution, etc.) recently discussed in the IRTF/IETF should be

   *  In section of 'Sample Application Scenarios', to dig deeper into
      one or two use cases.

15.  References

15.1.  Normative References

   [RFC7622]  Saint-Andre, P., "Extensible Messaging and Presence
              Protocol (XMPP): Address Format", RFC 7622,
              DOI 10.17487/RFC7622, September 2015,

   [RFC8345]  Clemm, A., Medved, J., Varga, R., Bahadur, N.,
              Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
              Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
              2018, <>.

   [RFC8346]  Clemm, A., Medved, J., Varga, R., Liu, X.,
              Ananthakrishnan, H., and N. Bahadur, "A YANG Data Model
              for Layer 3 Topologies", RFC 8346, DOI 10.17487/RFC8346,
              March 2018, <>.

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   [RFC8639]  Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
              E., and A. Tripathy, "Subscription to YANG Notifications",
              RFC 8639, DOI 10.17487/RFC8639, September 2019,

   [RFC8641]  Clemm, A. and E. Voit, "Subscription to YANG Notifications
              for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
              September 2019, <>.

   [RFC8944]  Dong, J., Wei, X., Wu, Q., Boucadair, M., and A. Liu, "A
              YANG Data Model for Layer 2 Network Topologies", RFC 8944,
              DOI 10.17487/RFC8944, November 2020,

   [RFC9114]  Bishop, M., Ed., "HTTP/3", RFC 9114, DOI 10.17487/RFC9114,
              June 2022, <>.

15.2.  Informative References

   [Dai2020]  Dai, Y. Dai., Zhang, K. Zhang., Maharjan, S. Maharjan.,
              and Yan Zhang. Zhang, "Deep Reinforcement Learning for
              Stochastic Computation Offloading in Digital Twin
              Networks. IEEE Transactions on Industrial Informatics,
              vol. 17, no. 17", August 2020.

   [DNA-2022] Zhang, P. Zhang., Gember-Jacobson, A. Gember-Jacobson.,
              Zuo, Y. Zuo., Huang, Y Huang., Liu, X. Liu., and H. Li.
              Li, "Differential Network Analysis, USENIX Symposium on
              Networked Systems Design and Implementation (NSDI 22)",
              April 2022.

   [Dong2019] Dong, R. Dong., She, C. She., HardjawanaLiu, W.
              Hardjawana., Li, Y. Li., and B. Vucetic. Vucetic, "Deep
              Learning for Hybrid 5G Services in Mobile Edge Computing
              Systems: Learn from a Digital Twin. IEEE Transactions on
              Wireless Communications,vol. 18, no. 10", July 2019.

   [DTPI2021] "IEEE International Conference on Digital Twins and
              Parallel Intelligence - Digital Twin Network Session,
    ", July 2021.

   [EVE-NG]   "Emulated Virtual Environment Next Generation, EVE-NG.

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              Fuller, A. Fuller., Fan, Z., Day, C., and C. Barlow,
              "Digital Twin: Enabling Technologies, Challenges and Open
              Research," in IEEE Access, vol. 8, pp. 108952-108971",

              Ros-Giralt, J. Ros-Giralt., Amsel, N. Amsel., Yellamraju,
              S. Yellamraju., Ezick, J. Ezick., Lethin, R. Lethin.,
              Jiang, Y. Jiang., Feng, A. Feng., Tassiulas, L.
              Tassiulas., Wu, Z. Wu., and K, Bergman. Bergman,
              "Designing data center networks using bottleneck
              structures", ACM SIGCOMM", August 2021.

              Ros-Giralt, J. Ros-Giralt., Bohara, A. Bohara.,
              Yellamraju, S. Yellamraju., Langston, H. Langston.,
              Lethin, R. Lethin., Jiang, Y. Jiang., Tassiulas, L.
              Tassiulas., Li, J. Li., Tan, Y. Tan., and M.
              Veeraraghavan. Veeraraghavan, "On the Bottleneck Structure
              of Congestion-Controlled Networks, ACM SIGMETRICS",
              December 2019.

   [GNS3]     "Graphical Network Simulator-3, GNS3.

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

   [Hong2021] Hong, H., Wu, Q., Dong, F., Song, W., Sun, R., Han, T.,
              Zhou, C., and H. Yang, "NetGraph: An Intelligent Operated
              Digital Twin Platform for Data Center Networks. In ACM
              SIGCOMM 2021 Workshop on Network-Application Integration
              (NAI' 21), Virtual Event, USA. ACM, New York, NY, USA",

   [Hu2021]   Hu, W., Zhang, T., Deng, X., Liu, Z., and J. Tan, "Digital
              twin: a state-of-the-art review of its enabling
              technologies, applications and challenges. Journal of
              Intelligent Manufacturing and Special Equipment, Vol. 2
              No. 1, pp. 1-34", 2021.

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              Claise, B., Quilbeuf, J., Diego Lopez, R., Dominguez, I.,
              and T. Graf, "A Data Manifest for Contextualized Telemetry
              Data", Work in Progress, Internet-Draft, draft-claise-
              opsawg-collected-data-manifest-04, 25 July 2022,

              Ros-Giralt, J., Yellamraju, S., Wu, Q., Contreras, L. M.,
              Yang, Y. R., and K. Gao, "Supporting Bottleneck Structure
              Graphs in ALTO: Use Cases and Requirements", Work in
              Progress, Internet-Draft, draft-giraltyellamraju-alto-bsg-
              requirements-03, 23 September 2022,

              Wu, Q., Song, W., Liu, P., Ma, Q., Wang, W., and Z. Niu,
              "Adaptive Subscription to YANG Notification", Work in
              Progress, Internet-Draft, draft-ietf-netconf-adaptive-
              subscription-01, 21 October 2022,

              Zhou, C., Chen, D., and P. Martinez-Julia, "Data
              Collection Requirements and Technologies for Digital Twin
              Network", Work in Progress, Internet-Draft, draft-zcz-
              nmrg-digitaltwin-data-collection-00, 10 July 2022,

   [ISO-2021] ISO, "Digital Twin manufacturing framework - Part 2:
              Reference architecture: ISO/CD 23247-2.
    ", 2021.

              Madni, A. Madni., Madni, C. Madni., and S. Lucero. Lucero,
              "Leveraging digital twin technology in model-based systems
              engineering. Systems, vol. 7, no. 1, p. 7", January 2019.

   [MimicNet] Zhang, Q. Zhang., NG, K. K.W. NG., Kazer, C. W. Kazer.,
              Yan, S. Yan., Sedoc, J. Sedoc., and V. Liu. Liu,
              "MimicNet: Fast Performance Estimates for Data Center
              Networks with Machine Learning. In ACM SIGCOMM 2021
              Conference (SIGCOMM ’21).", August 2021.

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   [Mininet]  "Mninet: An Instant Virtual Network on your Laptop,

              Natis, Y. Natis., Velosa, A. Velosa., and W. R. Schulte.
              Schulte, "Innovation insight for digital twins - driving
              better IoT-fueled decisions.
    ", 2017.

              Nguyen, H. X. Nguyen., Trestian, R. Trestian., To, D. To.,
              and M. Tatipamula. Tatipamula, "Digital Twin for 5G and
              Beyond. IEEE Communications Magazine, vol. 59, no. 2",
              February 2021.

   [NS-3]     "Network Simulator, NS-3.".

   [RESTFul]  Richardson, L. Richardson. and M. Amundsen. Amundsen,
              "RESTful Web APIs. O'Reilly Media, Inc.", 2013.

   [RFC6241]  Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
              and A. Bierman, Ed., "Network Configuration Protocol
              (NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,

   [RFC9232]  Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
              A. Wang, "Network Telemetry Framework", RFC 9232,
              DOI 10.17487/RFC9232, May 2022,

   [RFC9315]  Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", RFC 9315, DOI 10.17487/RFC9315, October
              2022, <>.

              Rosen, R. Rosen., Wichert, G. Von Wichert., Lo, G. Lo.,
              and K.D. Bettenhausen. Bettenhausen, "About the importance
              of autonomy and DTs for the future of manufacturing. IFAC-
              Papersonline, Vol. 48, pp. 567-572.", 2015.

Zhou, et al.              Expires 27 April 2023                [Page 27]
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   [RouteNet] Rusek, K. Rusek., Suárez-Varela, J. Suárez-Varela.,
              Almasan, P. Almasan., Barlet-Ros, P. Barlet-Ros., and A.
              Cabellos-Aparicio. Cabellos-Aparicio, "RouteNet:
              Leveraging Graph Neural Networks for network modeling and
              optimization in SDN. IEEE Journal on Selected Areas in
              Communication (JSAC), vol. 38, no. 10", October 2020.

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

   [TNT2022]  "IEEE International workshop on Technologies for Network
              2022", 2022.

Appendix A.  Change Logs

   v06 - v07: Addressed reviewer's comments from adoption call,
   including below major changes.

   *  Resequenced the sections via adding more subsections on concepts
      of digital twin network, removing the 'Requirements Language'
      section, and moving ahead the 'Challenges' section.

   *  Cited more papers, or industrial information on digital twin
      concepts and digital twin for networks.

   *  Added more information on describing the challenges and key
      characteristics digital twin network.

   *  Removed previous open issue on investigating related digital twin
      network work and identify the differences and commonalities, and
      added several new open issues for future study.

   *  Other editorial changes.

   v05 - v06: Addressed comments form meeting and maillist, to request
   adoptoin call.

   *  Remove acronym DTN to avoid conflict with 'Delay Tolerant

   *  Elaborate the descriptoin of Digital Twin Network architecture
      that supports multiple instances;

   *  Other Editorial changes.

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   04 - v05

   *  Clarify the difference between digital twin network platform and
      traditional network management system;

   *  Add more references of researches on applying digital twin to
      network field;

   *  Clarify the benefit of 'Privacy and Regulatory Compliance';

   *  Refine the description of reference architecture;

   *  Other Editorial changes.

   v03 - v04

   *  Update data definition and models definitions to clarify their

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

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

   *  Add explanation text for continuous verification.

   v02 - v03

   *  Split interaction with IBN part as a separate section.

   *  Fill security section;

   *  Clarify the motivation in the introduction section;

   *  Use new boilerplate for requirements language section;

   *  Key elements definition update.

   *  Other editorial changes.

   *  Add open issues section.

   *  Add section on application scenarios.

Authors' Addresses

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   Cheng Zhou
   China Mobile

   Hongwei Yang
   China Mobile

   Xiaodong Duan
   China Mobile

   Diego Lopez
   Telefonica I+D

   Antonio Pastor
   Telefonica I+D

   Qin Wu
   101 Software Avenue, Yuhua District
   Jiangsu, 210012

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   Mohamed Boucadair
   Rennes 35000

   Christian Jacquenet
   Rennes 35000

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