Digital Twin Network: Concepts and Reference Architecture
draft-zhou-nmrg-digitaltwin-network-concepts-05
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| Authors | Cheng Zhou , Hongwei Yang , Xiaodong Duan , Diego Lopez , Antonio Pastor , Qin Wu , Mohamed Boucadair , Christian Jacquenet | ||
| Last updated | 2021-10-25 (Latest revision 2021-07-07) | ||
| Replaced by | draft-irtf-nmrg-network-digital-twin-arch, draft-irtf-nmrg-network-digital-twin-arch | ||
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draft-zhou-nmrg-digitaltwin-network-concepts-05
Internet Research Task Force C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: April 28, 2022 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
October 25, 2021
Digital Twin Network: Concepts and Reference Architecture
draft-zhou-nmrg-digitaltwin-network-concepts-05
Abstract
Digital Twin technology has been seen as a rapid adoption technology
in Industry 4.0. The application of Digital Twin technology in the
networking field is meant to realize efficient and intelligent
management and accelerate network innovation.
This document presents an overview of the concepts of Digital Twin
Network (DTN), provides the basic definitions and a reference
architecture, lists a set of application scenarios, and discusses the
benefits and key challenges of such technology.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Task Force (IETF). Note that other groups may also distribute
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 28, 2022.
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Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
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described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Requirements Language . . . . . . . . . . . . . . . . . . . . 3
3. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 4
4. Definition of Digital Twin Networks . . . . . . . . . . . . . 4
5. Benefits of Digital Twin Networks . . . . . . . . . . . . . . 6
5.1. Optimized Network Total Cost of Operation . . . . . . . . 7
5.2. Optimized Decision Making . . . . . . . . . . . . . . . . 7
5.3. Safer Assessment of Innovative Network Capabilities . . . 7
5.4. Privacy and Regulatory Compliance . . . . . . . . . . . . 8
5.5. Customized Network Operation Training . . . . . . . . . . 8
6. Reference Architecture of Digital Twin Network . . . . . . . 8
7. Challenges to Build Digital Twin Networks . . . . . . . . . . 11
8. Interaction with IBN . . . . . . . . . . . . . . . . . . . . 12
9. Application Scenarios . . . . . . . . . . . . . . . . . . . . 12
9.1. Human Training . . . . . . . . . . . . . . . . . . . . . 12
9.2. ML Training . . . . . . . . . . . . . . . . . . . . . . . 13
9.3. DevOps-Oriented Certification . . . . . . . . . . . . . . 13
9.4. Network Fuzzing . . . . . . . . . . . . . . . . . . . . . 13
10. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
11. Security Considerations . . . . . . . . . . . . . . . . . . . 14
12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 14
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
14. Open issues . . . . . . . . . . . . . . . . . . . . . . . . . 15
15. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
15.1. Normative References . . . . . . . . . . . . . . . . . . 15
15.2. Informative References . . . . . . . . . . . . . . . . . 15
Appendix A. Change Logs . . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 17
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1. Introduction
The fast growing of network scale and the increased demand placed on
these networks, requires them to accommodate and adapt dynamically to
customer needs, implying a big 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 full business data-driven.
So far, this paradigm 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]. See more in Section 4.
A digital twin network 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 network is an expansion platform of network simulation.
The main difference compared to traditional network management system
is the use of interactive virtual-real mapping to build closed-loop
network automation. 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.
Having an emulation platform that allows to reliably represent the
state of a network is more dependable than a simulation platform.
The emulated platform can, thus, be used to 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. Service impact analysis tasks will
also be facilitated.
2. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
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14 [RFC2119][RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Definitions and Acronyms
PLM: Product Lifecycle Management
IBN: Intent-Based Networking
AI: Artificial Intelligence
ML: Machine Learning
OAM: Operations, Administration, and Maintenance
CI/CD: Continuous Integration / Continuous Delivery
4. Definition of Digital Twin Networks
The concept of a virtual equivalent to a physical product or the
digital twin was first introduced in the Product Lifecycle Management
(PLM) course in 2003 by Scholar Michael Grieves [Grieves2014]. It
has been since then widely acknowledged in both industry and academic
publications. And some researchers have also tried to apply the
concept of digital twin to the networking field, such as [Dong2019],
[Dai2020] and [Nguyen2021]. However, there is no standard definition
of "digital twin network" within the networking industry and SDOs.
This document defines digital twin network as a virtual
representation of the physical network. Such virtual representation
of the network is meant to be used to analyze, diagnose, emulate, and
then control the physical network based on data, models, and
interfaces. To that aim, a real-time and interactive mapping is
required between the physical network and its virtual twin network.
As shown in Figure 1, the digital twin network involves four key
technology elements: data, mapping, models, and interfaces.
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+-------------+ +--------------+
| | | |
| Mapping | | Interface |
| | | |
+-------------+-----------------+--------------+
| |
| Analyze, Diagnose |
| |
| +----------------------+ |
| | NETWORK DIGITAL TWIN | |
| +----------------------+ |
+------------+ +------------+
| | Emulate, Control | |
| Models | | Data |
| |------------------------| |
+------------+ +------------+
Figure 1: Key Elements of Digital Twin Network
Data: A digital twin network should maintain historical data and/or
real time data (configuration data, operational state data,
topology data, trace data, metric data, process data, etc.) about
its real-world twin (i.e., physical network) that are required by
the models to represent and understand the states and behaviors of
the real-world twin. The data is characterized as the single
source of "truth" and populated in the data repository, which
provides timely and accurate data service support for building
various models.
Models: Techniques that involve collecting data from one or more
sources in the real-world twin and developing a comprehensive
representation of the data (e.g., system, entity, process) using
specific models. They 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.
Interfaces: Standardized interfaces can ensure the interoperability
of digital twin network. There are two major types of interfaces:
* The interface between the digital twin network platform and the
physical network infrastructure.
* The interface between digital twin network platform and
applications.
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The former provides real time data collection and control on the
physical network. The latter helps deliver application requests
to the digital twin network platform and expose the various
platform capabilities to applications.
Mapping: Is 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 provides 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:
o repeatability: that is the capacity to replicate network
conditions on-demand.
o reproducibility: i.e., the ability to replay successions of
events, possibly under controlled variations.
5. Benefits of Digital Twin Networks
Digital twin networks 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 can safely exercise the enforcement of
network planning policies, deployment procedures, etc., without
jeopardizing the daily operation of the physical network.
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The benefits of digital twin network can be categorized as follows:
lower cost of network, optimized and safer decision-making, safer
testing of innovative network capabilities (including "what-if"
scenarios), privacy and regulatory compliance, and customized network
operation training. The following subsections further elaborate on
such benefits.
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 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.
5.2. Optimized Decision Making
Traditional network operation and management mainly focus on
deploying and managing running services, but hardly support
predictive maintenance techniques.
Digital twin network can combine data acquisition, big data
processing, and AI modeling to assess the status of the network, but
also to predict future trends, and better organize predictive
maintenance. The ability to reproduce network behaviors under
various conditions facilitates the corresponding assessment of the
various evolution options as often as required.
5.3. Safer Assessment of Innovative Network Capabilities
Testing a new feature in an operational network is not only complex,
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.,
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99.999%) and the slow learning speed or phase-in learning steps of
AI/ML algorithms. With digital twin networks, 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 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.
5.5. 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.
6. Reference Architecture of Digital Twin Network
Based on the definition of the key digital twin network technology
elements introduced in Section 4, a digital twin network architecture
is depicted in Figure 2. This digital twin network architecture is
broken down into three layers: Application Layer, Network Digital
Twin Layer, and Physical Network Layer.
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+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure| Intent Input
| |
+-------------+-------------------v-----------------------+
| Instance of Network Digital Twin|
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Entity | |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^----------------------------+-------------------+
| |
| data collection | control
+--------+----------------------------v-------------------+
| Physical Network |
| |
+---------------------------------------------------------+
Figure 2: Reference Architecture of Digital Twin Network
1. Physical Network: (All or relevant) network elements in the
physical network exchange massive network data and control with
network digital twin entity, through twin southbound interfaces.
As the physical object of the network twin, the physical network
can be a mobile access network, a transport network, a mobile
core, a backbone, etc. The network can also be a data center
network, a campus enterprise network, an industrial Internet of
Things, etc. The network can span across a single network domain
or multiple network domains.
2. The Intermediate layer is the Network Digital Twin. This layer
includes three key subsystems: Data Repository subsystem, Service
Mapping Models subsystem, and Digital Twin Entity Management
subsystem.
* Data Repository subsystem is responsible for collecting and
storing various network data for building various models by
collecting and updating the real-time operational data of
various network elements through the twin southbound
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interface, and providing data services (e.g., fast retrieval,
concurrent conflict 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 Entity Management fulfils the management function
of digital twin network, records the life-cycle transactions
of the entity, monitors the performance and resource
consumption of the 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
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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.
3. Application Layer: Various applications (e.g., OAM, IBN) can
effectively run over a digital twin network platform to implement
either conventional or innovative network operations, with low
cost and less service impact on real networks. Network
applications 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. Challenges to Build Digital Twin Networks
As mentioned in Section 5, digital twin networks can bring many
benefits to network management as well as facilitate the introduction
of innovative network capabilities. However, building an effective
and efficient digital twin network system remains a challenge. The
following is a list of major challenges:
o 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 models. The
requirements of software and hardware of the digital twin network
system will be even more constraining.
o Interoperability: It is difficult to establish a unified digital
twin network system with a unified data model in the whole network
domain due to the inconsistency of technical implementations and
the heterogeneity of vendor technologies.
o Data modeling difficulties: Based on large-scale network data,
data modeling should not only focus on ensuring the accuracy of
model functions, but also need to consider the flexibility and
scalability of the model. Balancing these requirements further
increase the complexity of building efficient and hierarchical
functional data models.
o Real-time requirements: For services with real-time requirements,
the processing of model simulation and verification through a
digital twin network will increase the service delay, so the
function and process of the data model need to be based on
automated processing mechanism under various network application
scenarios; at the same time, the real-time requirements will
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further increase performance requirements on the system software
and hardware.
o Security risks: A digital twin network has to synchronize all the
data of involved physical networks in real time, which inevitably
augments the attack surface, with a higher risk of information
leakage, in particular.
To address these challenges, digital twin networks need continuous
optimization and breakthrough on key enabling technologies including
data acquisition, data storage, data modeling, network visualization,
interface standardization, and security assurance, so as to meet the
requirements of compatibility, reliability, real-time, and security.
8. Interaction with IBN
Implementing Intent-Based Networking (IBN) is an innovative
technology for life-cycle network management. Future networks will
be possibly Intent-based, which means that users can input their
abstract 'intent' to the network, instead of detailed policies or
configurations on the network devices.
[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 networks can be an important enabler platform to implement IBN
systems and speed up their deployment.
9. Application Scenarios
Digital twin network can be applied to solve different problems in
network management and operation.
9.1. Human Training
The usual approach to network Operations, Administration, and
Maintenance (OAM) with procedures applied by humans is open to errors
in all these procedures, with impact in network availability and
resilience. Response procedures and actions for most relevant
operational requests and incidents are commonly defined to reduce
errors to a minimum. The progressive automation of these procedures,
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such as predictive control or closed-loop management, reduce the
faults and response time, but still there is the need of a human-in-
the-loop for multiples actions. These processes are not intuitive
and require training to learn how to respond.
The use of digital twin network for this purpose in different network
management activities will improve the operators performance. One
common example is cybersecurity incident handling, where "cyber-
range" exercises are executed periodically to train security
practitioners. Digital twin network will offer realistic
environments, fitted to the real production networks.
9.2. ML Training
Machine Learning requires data and their context to be available in
order to apply it. A common approach in the network management
environment has been to simulate or import data in a specific
environment (the ML developer lab), where they are used to train the
selected model, while later, when the model is deployed in
production, re-train or adjust to the production environment context.
This demands a specific adaption period.
Digital twin network simplifies the complete ML lifecycle development
by providing a realistic environment, including network topologies,
to generate the data required in a well-aligned context. Dataset
generated belongs to the digital twin network and not to the
production network, allowing information access by third parties,
without impacting data privacy.
9.3. DevOps-Oriented Certification
The potential application of CI/CD models network management
operations increases the risk associated to deployment of non-
validated updates, what conflicts with the goal of the certification
requirements applied by network service providers. A solution for
addressing these certification requirements is to verify the specific
impacts of updates on service assurance and SLAs using a digital twin
network environment replicating the network particularities, as a
previous step to production release.
Digital twin network control functional block supports such dynamic
mechanisms required by DevOps procedures.
9.4. Network Fuzzing
Network management dependency on programmability increases systems
complexity. The behavior of new protocol stacks, API parameters, and
interactions among complex software components are examples that
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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.
10. Summary
Research on digital twin network has just started. This document
presents an overview of the digital twin network concepts. Looking
forward, further elaboration on digital twin network scenarios,
requirements, architecture, and key enabling technologies should be
investigated by the industry, so as to accelerate the implementation
and deployment of digital twin networks.
11. Security Considerations
This document describes concepts and definitions of digital twin
network. As such, the following security considerations remain high
level, i.e., in the form of principles, guidelines or requirements.
Security considerations of the digital twin network include:
o Secure the digital twin system itself.
o Data privacy protection.
Securing the digital twin network system aims at making the digital
twin system operationally secure by implementing security mechanisms
and applying security best practices. In the context of digital twin
network, such mechanisms and practices may consist in data
verification and model validation, mapping operations between
physical network and digital counterpart network by authenticated and
authorized users only.
Synchronizing the data between the physical and the digital twin
networks may increase the risk of sensitive data and information
leakage. Strict control and security mechanisms must be provided and
enabled to prevent data leaks.
12. Acknowledgements
Diego Lopez and Antonio Pastor were partly supported by the European
Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
and grant agreement no. 871808 (INSPIRE-5Gplus).
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13. IANA Considerations
This document has no requests to IANA.
14. Open issues
o Investigate related digital twin network work and identify the
differences and commonalities, e.g., how is this concept and
architecture different from digital twin for industry application?
15. References
15.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
15.2. Informative References
[Dai2020] Dai, Y., Zhang, K., Maharjan, S., and Yan. Zhang, "Deep
Reinforcement Learning for Stochastic Computation
Offloading in Digital Twin Networks. IEEE Transactions on
Industrial Informatics, vol. 17, no. 17", August 2020.
[Dong2019]
Dong, R., She, C., HardjawanaLiu, W., Li, Y., and B.
Vucetic, "Deep Learning for Hybrid 5G Services in Mobile
Edge Computing Systems: Learn from a Digital Twin. IEEE
Transactions on Wireless Communications,vol. 18, no. 10",
July 2019.
[Grieves2014]
Grieves, M., "Digital twin: Manufacturing excellence
through virtual factory replication", 2003.
[I-D.irtf-nmrg-ibn-concepts-definitions]
Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", draft-irtf-nmrg-ibn-concepts-definitions-05
(work in progress), September 2021.
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[Nguyen2021]
Nguyen, H., Trestian, R., To, D., and M. Tatipamula,
"Digital Twin for 5G and Beyond. IEEE Communications
Magazine, vol. 59, no. 2", February 2021.
[Tao2019] Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in
Industry: State-of-the-Art. IEEE Transactions on
Industrial Informatics, vol. 15, no. 4.", April 2019.
Appendix A. Change Logs
v04 - v05
o Clarify the difference between digital twin network platform and
traditional network management system;
o Add more references of researches on applying digital twin to
network field;
o Clarify the benefit of 'Privacy and Regulatory Compliance';
o Refine the description of reference architecture;
o Other Editorial changes.
v03 - v04
o Update data definition and models definitions to clarify their
difference.
o Remove the orchestration element and consolidated into control
functionality building block in the digital twin network.
o Clarify the mapping relation (one to one, and one to many) in the
mapping definition.
o Add explanation text for continuous verification.
v02 - v03
o Split interaction with IBN part as a separate section.
o Fill security section;
o Clarify the motivation in the introduction section;
o Use new boilerplate for requirements language section;
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o Key elements definition update.
o Other editorial changes.
o Add open issues section.
o Add section on application scenarios.
Authors' Addresses
Cheng Zhou
China Mobile
Beijing 100053
China
Email: zhouchengyjy@chinamobile.com
Hongwei Yang
China Mobile
Beijing 100053
China
Email: yanghongwei@chinamobile.com
Xiaodong Duan
China Mobile
Beijing 100053
China
Email: duanxiaodong@chinamobile.com
Diego Lopez
Telefonica I+D
Seville
Spain
Email: diego.r.lopez@telefonica.com
Antonio Pastor
Telefonica I+D
Madrid
Spain
Email: antonio.pastorperales@telefonica.com
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Qin Wu
Huawei
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Email: bill.wu@huawei.com
Mohamed Boucadair
Orange
Rennes 35000
France
Email: mohamed.boucadair@orange.com
Christian Jacquenet
Orange
Rennes 35000
France
Email: christian.jacquenet@orange.com
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