Internet-Draft Digital Twin Network Concept March 2022
Zhou, et al. Expires 6 September 2022 [Page]
Internet Research Task Force
Intended Status:
C. Zhou
China Mobile
H. Yang
China Mobile
X. Duan
China Mobile
D. Lopez
Telefonica I+D
A. Pastor
Telefonica I+D
Q. Wu
M. Boucadair
C. Jacquenet

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.

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This Internet-Draft will expire on 6 September 2022.

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.

2. Terminology

2.1. Acronyms & Abbreviations

Intent-Based Networking
Artificial Intelligence
Continuous Integration / Continuous Delivery
Machine Learning
Operations, Administration, and Maintenance
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 cycle.
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 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 [Tao2019].

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 and 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 can 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 better solutions beyond physical network. Since 2017, the application of digital twin technology in the field of communication networks has gradually been researched. Some examples are listed below.

In academy, [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. In addition, international workshops dedicated to digital twin in network field have already appeared, such as IEEE DTPI 2021 - Digital Twin Network Online Session [DTPI2021], or are being proposed such as 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 organic whole of the overall network system and become 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 network.

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.

    +-------------+                 +--------------+
    |             |                 |              |
    |  Mapping    |                 |  Interface   |
    |             |                 |              |
             |                          |
             |    Analyze, Diagnose     |
             |                          |
             | +----------------------+ |
             | | Digital Twin Network | |
             | +----------------------+ |
 +------------+                        +------------+
 |            |   Emulate, Control     |            |
 |   Models   |                        |    Data    |
 |            |------------------------|            |
 +------------+                        +------------+
Figure 1: Key Elements of Digital Twin Network

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.


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 decision-making.

Various models such as service models, data models, dataset models, or knowledge graph can be used to represent the physical network assets and, then, instantiated to serve various network applications.


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.
  • The interface between digital twin network platform and applications.

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


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 network.

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.

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:

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

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.

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 mantaining digital twins can be summarized as the following five aspects:

  • Data acquisition and processing
  • High-fidelity modeling
  • Real-time, two-way connection between the virtual and the real twins
  • 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 connection are relatively easy to achieve. On the other hand, there are many kinds of network elements and topologies in the network field; and the complex giant system of network carries a variety of business services. So, the construction of a digital twin network system needs to consdier 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, to lower the cost of virtual-real communication and data storage, efficient tools on data collection and data compression methods must be used.
Due to the inconsistency of technical implementations and the heterogeneity of vendor 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.
Real-time requirements:
Network services normally have real-time requirements, the processing of model simulation and verification through a digital twin network will increase the service latency. Meanwhile, the real-time requirements will further increase performance requirements on the system software and hardware. Moreover, it is also challenge to keep network digital twins in sync given the nature of distributed systems and propagation delays. 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.

|   +-------+   +-------+          +-------+              |
|   | 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 datacenter network.

Digital Twin Layer:

This layer includes three key subsystems: Data Repository subsystem, Service Mapping Models subsystem, and Digital Twin Network Management subsystem.

One or multiple digital twin network instances can be built and maintained:

  • 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'.

  • 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. 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. [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the concept of "Intent" and provides an overview of IBN functionalities. The key characteristic of an IBN system is that user intent can be assured automatically via continuously adjusting the policies and validating the real-time situation.

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

8. Sample Application Scenarios

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

8.1. 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.

8.2. 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.

8.3. DevOps-Oriented Certification

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

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

8.4. 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 configuration.

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 delivery.

9. 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 network.

10. 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.

11. 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).

12. IANA Considerations

This document has no requests to IANA.

13. Open issues

  • The draft focuses on concept and architecture of digital twin network, not including enabling technologies. Actually, each 'enabling technology' is worth of a separate draft to study in details in future. A decision is needed that whether to add a section to describe the enabling technologies in brief.
  • Related to above issue, if section of enabling technologies is added, recent technologies (e.g. Network connectivity, Real-time data communication, Collaboration management, conflict detection and resolution, etc.) recently discussed in the IRTF/IETF should be described.
  • In section of 'Sample Application Scenarios', to dig deeper into one or two use cases.
  • On the research side, the idea behind digital twin networks is reminiscent of earlier work from the 1990s that should be referenced/acknowledged. Examples include the Shadow MIB concept, Inductive Modeling Technique, etc.

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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 studys.
  • Other Editorial changes.

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

  • Remove acronym DTN to avoid conflict with 'Delay Tolerant Network';
  • Elaborate the descriptoin of Digital Twin Network architecture that supports multiple instances;
  • Other Editorial changes.

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 difference.
  • 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

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
Mohamed Boucadair
Rennes 35000
Christian Jacquenet
Rennes 35000