Network Management                                               C. Zhou
Internet-Draft                                                   H. Yang
Intended status: Informational                                   X. Duan
Expires: 5 September 2024                                   China Mobile
                                                                D. Lopez
                                                               A. Pastor
                                                          Telefonica I+D
                                                                   Q. Wu
                                                            M. Boucadair
                                                            C. Jacquenet
                                                            4 March 2024

       Network Digital Twin: 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.

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Table of Contents

   1.  Introduction
   2.  Terminology
     2.1.  Acronyms & Abbreviations
     2.2.  Definitions
   3.  Introduction of Concepts
     3.1.  Background of Digital Twin
     3.2.  Digital Twin for Networks
   4.  Characteristics of Network Digital Twin
   5.  Benefits of Network Digital Twin
     5.1.  Optimized Network Total Cost of Operation
     5.2.  Optimized Decision Making
     5.3.  Safer Assessment of Innovative Network Capabilities
     5.4.  Privacy and Regulatory Compliance
     5.5.  Customized Network Operation Training
   6.  Challenges to Build Network Digital Twin
   7.  A Reference Architecture of Network Digital Twin
   8.  Enabling Technologies to Build Network Digital Twin
     8.1.  Data Collection and Data Services
     8.2.  Network Modeling
     8.3.  Network Visualization
     8.4.  Interfaces
     8.5.  Twinning Management
   9.  Interaction with Intent-Based Networking (IBN)
   10. Sample Application Scenarios
     10.1.  Human Training
     10.2.  Machine Learning Training
     10.3.  DevOps-Oriented Certification
     10.4.  Network Fuzzing
     10.5.  Network Inventory Management
   11. Research Perspectives: A Summary
   12. Security Considerations
   13. IANA Considerations
   14. Open issues
   15. Informative References
   Authors' Addresses

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 and adopted in Industry 4.0.
   See more in Section 3.

   A digital twin for networks can be built by applying Digital Twin
   technologies to networks and creating a virtual image of real network
   facilities (called herein, emulation).  Basically, the digital twin
   for networks is an expansion platform of network emulation and can be
   seen as a tool for scenario planning, impact analysis, and change
   management.  The main difference compared to conventional network
   simulation is the interactive virtual-real mapping and data driven
   approach to build closed-loop network automation.  By integrating
   network digital twin into the network management, it allows network
   maintenance engineers to assess, model, and tweak optimization
   strategies in a risk-free environment, ensuring that only the most
   effective changes might be implemented in the real network (i.e.,
   subject to adequate validation and control checks).  Digital twin for
   networks also play a crucial role in root cause analysis, providing a
   sandbox for assessing hypotheses and validating the outcomes of data-
   driven insights without impacting end users, when adequate isolation
   guards are in place.  Therefore, digital twin for networks is more
   than an simulation platform or network simulator.

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

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:  Digital counterpart of a physical system (twin) that
      captures its attributes, behavior, and interactions and is
      (continually) updated with the latter's performance, maintenance,
      and health status data throughout the physical system's life

   Network digital twin:  A digital representation that is used in the
      context of Networking and whose physical counterpart is a data
      network or enterprise network.  This is also called, digital twin
      for networks.  See more in Section 4.

   Physical network:  Object, system, process, software, or environment
      that the digital twin is designed to replicate and represent

3.  Introduction of Concepts

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
   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 (2024), 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 real network.
   The motivation for network digital twin 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
   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.  [Dai2022] gives the concept of digital twin and proposes
   an digital twin-enabled vehicular edge computing (VEC) network, where
   digital twin can enable adaptive network management via the two-
   closed loops between physical VEC networks and digital twins.  In
   addition, international workshops dedicated to digital twin in
   networking field have already appeared, such as IEEE DTPI 2021&2022-
   Digital Twin Network Online Session [DTPI2021], [DTPI2022], 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 network digital twin can be regarded as
   an indispensable part of the overall network system and provides a
   general architecture involving the whole life cycle of real 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

4.  Characteristics of Network Digital Twin

   So far, there is no standard definition for characteristic of
   "network digital twin" within the networking industry.  This document
   introduces four key elements (i.e., data, models, mapping, and
   interfaces) to characterize the network digital twin.  These four
   elements can be integrated into a network management system to
   analyze, diagnose, emulate, and control the real network.  To that
   aim, a real-time and interactive mapping is required between the real
   network and its virtual twin network.  Whether a Digital Twin
   supports all or a subset of the functions above (i.e., analyze,
   diagnose, emulate, and control) is deployment specific.

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

                  +-------------+                 +--------------+
                  |             |                 |              |
                  |  Mapping    |                 |  Interface   |
                  |             |                 |              |
                           |                          |
                           |    Analyze, Diagnose     |
                           |                          |
                           | +----------------------+ |
                           | | Network Digital Twin | |
                           | +----------------------+ |
               +------------+                        +------------+
               |            |   Emulate, Control     |            |
               |   Models   |                        |    Data    |
               |            |------------------------|            |
               +------------+                        +------------+

               Figure 1: Key Elements of Network Digital Twin

   Data:  A network digital twin 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. real 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, or process)
      using specific models.  These models are used as emulation and
      diagnosis basis to provide dynamics and elements on how the live
      real 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 real
      network assets and, then, instantiated to serve various network

   Interfaces:  Standardized interfaces ensure the interoperability of
      network digital twin.  There are two major types of interfaces:

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

   *  The interface between network digital twin platform and

   : The former provides real-time data collection and control on the
   real network.  The latter helps in delivering application requests to
   the network digital twin 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 real network and the twin network or between two twin
      networks.  The mapping can be:

      *  One to one (pairing, vertical): Synchronize between a real
         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 real network.  It also allows using the digital twin to
      optimize the performance and maintenance of the real network.

   The network digital twin constructed based on the four core
   technology elements can analyze, diagnose, emulate, and control the
   real 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 network digital twin
   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.

   and "the mirroring pace and scope" should be controlled for a given
   twin instance.

      Note: Realtime interaction is not always mandatory for all twins.
      For example, when assessing some configuration changes or
      emulating some innovative techniques, the digital twins can behave
      as an isolated simulation platform without the need of realtime
      telemetry data.  It might be useful to have interactive mapping
      capability so that the validated changes can be evaluated under
      real network conditions whenever required by the testers.  Whether
      realtime interaction between virtual and real network is mandatory
      is a configurable parameter.  Adequate validation guards have to
      be enforced at both twin and physical network.  Enabling realtime
      interaction in network digital twin is a catalyst to achieve
      autonomous networks or self-driven network.

5.  Benefits of Network Digital Twin

   Network digital twin 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 real network.

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

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

   (b)  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.

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

   The following subsections further elaborate such benefits in details.

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

5.2.  Optimized Decision Making

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

   Network digital twin 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,
   but also extremely risky.  Service impact analysis is required to be
   adequately achieved prior to effective activation of a new feature.

   Network digital twin can greatly help assessing innovative network
   capabilities without jeopardizing the daily operation of the real
   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 network digital twin, 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.

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 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 network digital twin,
   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.

5.5.  Customized Network Operation Training

   Network architectures can be complex, and their operation requires
   expert personnel.  Network digital twin 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.

6.  Challenges to Build Network Digital Twin

   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

   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 network digital twin
      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 network digital twin, 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.

   Real-time requirements:  Network services normally have real-time
      requirements, the processing of model simulation and verification
      through a network digital twin 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 real network and network digital twin.

      Changes to the digital object automatically drive changes in the
      real 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 network digital twin has to synchronize all or
      subset of the data related to involved real 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.

   To address the above listed challenges, it is important to agree on a
   unified architecture of network digital twin, which defines the main
   functional components and interfaces (Section 7).  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.

7.  A Reference Architecture of Network Digital Twin

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

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

          Figure 2: Reference Architecture of Network Digital Twin

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

      The real network can span across a single network administrative
      domain or multiple network administrative domains.  And, the real
      network can include both physical entities and some virtual
      entities (e.g. vSwitches, NFVs, etc.), which together carry
      traffic and provide actual network services.

      This document focuses on the IETF related real 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
      Network Digital Twin 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
      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 network digital twin 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 real 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’.

   *  Network Digital Twin Management fulfils the management function of
      network digital twin, 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
      network-facing interfaces between virtual and real network.  From
      implementation perspective, they may form a sub-layer or sub-
      system to provide common data collection and change control
      functions, enabled by a specific infrastructure supporting bi-
      directional flows and facilitating data aggregation, action
      translation, pre-processing, and ontologies.  It might not be
      possible or necessary to 'synchronize' all twin state or flows
      from twin entity to physical entity or network management system.
      Bi-directional interaction means that: data, state, or flows are
      reported or collected from the physical network or the network
      management system to a twin instance, and configure changes or
      'necessary' data sent from a twin instance to physical.

   Application Layer: Various applications (e.g., Operations,
   Administration, and Maintenance (OAM)) can effectively run over a
   network digital twin 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 network digital twin.  Such requests are exchanged
   through a northbound interface, so they are applied by service
   emulation at the appropriate twin instance(s).

8.  Enabling Technologies to Build Network Digital Twin

   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

8.1.  Data Collection and Data Services

   Data collection technology is the foundation of building data
   repository for network digital twin.  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 network digital twin.  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], and 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
   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.

8.2.  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
   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

8.3.  Network Visualization

   It is the internal requirement of the network digital twin 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 real 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.  The related topology data
   can be acquired using solutions provided in [RFC8345], [RFC8346],
   [RFC8944], etc.  Meanwhile, network digital twin system can select
   different interaction methods or combinations of interaction methods
   to realize the visual dynamic interaction mapping 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 middleware.

8.4.  Interfaces

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

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

   (e)  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.

   (f)  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 [RFC7622] or HTTP/3.0

   All these interfaces are recommended to be open and standardized
   interfaces so as to 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.

8.5.  Twinning Management

   Twinning management is the key to the efficient deployment and
   potential value of network digital twin systems in production
   networks.  Twinning management technology inputs all information and
   data from each step of network business into the constructed model
   through the construction of digital threads for optimization,
   prediction, and guidance.  Then, the implementation results are
   analyzed to see if they meet expectations, and any actions are fed
   back to form a closed loop.  Twinning management involves various
   network components (e.g., controller, orchestrator) and domains
   (security, for example) from end to end, including, but not limited
   to, the following main technologies:

   *  Orchestration of twins: Manage and organize multiple twin model
      instances, including the creation, deletion, storage, version
      control, and deployment of model instances, and arrange required
      modeling resources as needed to maximize resource utilization

   *  Collaboration Management: Coordinate multiple participants, such
      as network administrators, data scientists, security teams, etc.,
      to ensure the accuracy and real-time performance of the twins.
      Involve collaborative tools, workflow design, data sharing, and
      permission control to promote cooperation and information sharing
      among all parties.

   *  Conflict Detection and Resolution: Identify and address conflicts
      including user intents, access control policies, or multiple
      applications interacting within the digtial twin netowrk system.
      Conflict detection and resolution techniques may use various
      mechanisms, such as rule-based policies, role-based access
      control, or dynamic conflict resolution algorithms (e.g.
      [Pradeep2022] and [Zheng2022]).

   *  Energy-Efficient Twinning: Focus on energy efficiency in digital
      twin network system.  It includes monitoring and optimizing the
      energy consumption of both network equipment and digital twin
      system operation, reducing the energy expenditure of network
      operation, and achieving the goal of green network.

9.  Interaction with Intent-Based Networking (IBN)

   Intent-based, 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

   IBN can be envisaged in a network digital twin context to show how
   network digital twin 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 network digital twin
   platform instead of directly on real network.  Therefore, the digital
   twin network can be an important enabler platform to implement IBN
   systems and fooster their deployment.

10.  Sample Application Scenarios

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

10.1.  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 network digital twin 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.  Network digital twin will offer realistic
   environments, fitted to the real production networks.

10.2.  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.

   Network digital twin 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 network digital twin and not to the
   production network, allowing information access by third parties,
   without impacting data privacy.

10.3.  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 network digital twin environment replicating the
   network particularities, as a previous step to production release.

   Network digital twin control functional block supports such dynamic
   mechanisms required by DevOps procedures.

10.4.  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

   Network digital twin 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

10.5.  Network Inventory Management

   With the development of enterprise digitization, the number of
   enterprise Internet of Objects (IoT) devices, virtualized Cloud
   software inventory component (e.g., virtual firewall), and network
   hardware inventory (e.g., switches or 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.

   Network digital twin management can be used as a means to ensure
   consistent representation and reporting of inventory component 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 inventory components types connected
   to an enterprise network.  It also make actors on assets more
   accountable for breaching their compliance promises.  Special care
   should be considered to protect the inventory data since it may be
   gather privacy-sensitive information.

   The network inventory management for twins or various inventory
   components can be used, for example, to exercise the implication of
   End of Life (EoL), dependency, and hardware dependency "what-if"

11.  Research Perspectives: A Summary

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

12.  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 network digital twin include:

   *  Secure the digital twin system itself.

   *  Data privacy protection.

   Securing the network digital twin 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 real
   network and digital counterpart network by authenticated and
   authorized users only.

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

13.  IANA Considerations

   This document has no requests to IANA.

14.  Open issues

   Refer to:
   issues (

15.  Informative References

   [Dai2020]  IEEE Transactions on Industrial Informatics, "Deep
              Reinforcement Learning for Stochastic Computation
              Offloading in Digital Twin Networks", August 2020.

   [Dai2022]  Journal of Communications and Information Networks,
              "Adaptive Digital Twin for Vehicular Edge Computing and
              Networks", March 2022.

   [DNA-2022] NSDI 22, "Differential Network Analysis, USENIX Symposium
              on Networked Systems Design and Implementation", 2023.

   [Dong2019] IEEE Transactions on Wireless Communications, "Deep
              Learning for Hybrid 5G Services in Mobile Edge Computing
              Systems: Learn from a Digital Twin", July 2019.

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

   [DTPI2022] "IEEE International Conference on Digital Twins and
              Parallel Intelligence - Digital Twin Network Session",
              October 2022, <

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

              IEEE Access, "Digital Twin: Enabling Technologies,
              Challenges and Open Research", 2020.

              ACM SIGCOMM, "Designing data center networks using
              bottleneck structures", August 2021.

              ACM SIGMETRICS, "On the Bottleneck Structure of
              Congestion-Controlled Networks", December 2019.

   [GNS-3]    "Graphical Network Simulator-3, GNS3", n.d.,

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

   [Hong2021] ACM SIGCOMM 2021 Workshop on Network-Application
              Integration (NAI' 21), "NetGraph: An Intelligent Operated
              Digital Twin Platform for Data Center Networks", 2021.

   [Hu2021]   Journal of Intelligent Manufacturing and Special
              Equipment, "Digital twin: a state-of-the-art review of its
              enabling technologies, applications and challenges", 2021.

              Claise, B., Quilbeuf, J., Lopez, D., Martinez-Casanueva,
              I. D., and T. Graf, "A Data Manifest for Contextualized
              Telemetry Data", Work in Progress, Internet-Draft, draft-
              claise-opsawg-collected-data-manifest-06, 10 March 2023,

              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-04, 12 December 2023,

              Zhou, C., Chen, D., Martinez-Julia, P., and Q. Ma, "Data
              Collection Requirements and Technologies for Digital Twin
              Network", Work in Progress, Internet-Draft, draft-zcz-
              nmrg-digitaltwin-data-collection-03, 9 July 2023,

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

              "Leveraging digital twin technology in model-based systems
              engineering", January 2019.

   [MimicNet] ACM SIGCOMM 2021 Conference (SIGCOMM ’21), "MimicNet: Fast
              Performance Estimates for Data Center Networks with
              Machine Learning", 2021.

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

              "Innovation insight for digital twins - driving better
              IoT-fueled decisions", 2017,

              IEEE Communications Magazine, "Digital Twin for 5G and
              Beyond", February 2021.

   [NS-3]     "Network Simulator, NS-3", n.d., <>.

              "Conflict Detection and Resolution in IoT Systems: A
              Survey.  IoT 2022", February 2022.

   [RESTFul]  O'Reilly Media, Inc, "RESTful Web APIs", 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,

   [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, <>.

   [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, <>.

   [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, <>.

              IFAC-Papersonline, "About the importance of autonomy and
              DTs for the future of manufacturing", 2015.

   [RouteNet] IEEE Journal on Selected Areas in Communication (JSAC),
              "RouteNet:Leveraging Graph Neural Networks for network
              modeling and optimization in SDN", October 2020.

   [Tao2019]  IEEE Transactions on Industrial Informatics, "Digital Twin
              in Industry: State-of-the-Art", April 2019.

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

              "Intent Based Networking management with conflict
              detection and policy resolution in an enterprise network,
              Computer Networks, Volume 219", December 2022.


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

   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).

Authors' Addresses

   Cheng Zhou
   China Mobile

   Hongwei Yang
   China Mobile

   Xiaodong Duan
   China Mobile

   Diego Lopez
   Telefonica I+D

   Antonio Pastor
   Telefonica I+D

   Qin Wu

   Mohamed Boucadair

   Christian Jacquenet