Internet DRAFT - draft-irtf-nmrg-network-digital-twin-arch
draft-irtf-nmrg-network-digital-twin-arch
Internet Research Task Force C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: 27 April 2023 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
24 October 2022
Digital Twin Network: Concepts and Reference Architecture
draft-irtf-nmrg-network-digital-twin-arch-02
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 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 27 April 2023.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Acronyms & Abbreviations . . . . . . . . . . . . . . . . 4
2.2. Definitions . . . . . . . . . . . . . . . . . . . . . . . 4
3. Introduction and Concepts of Digital Twin Network . . . . . . 4
3.1. Background of Digital Twin . . . . . . . . . . . . . . . 4
3.2. Digital Twin for Networks . . . . . . . . . . . . . . . . 5
3.3. Definition of Digital Twin Network . . . . . . . . . . . 6
4. Benefits of Digital Twin Network . . . . . . . . . . . . . . 9
4.1. Optimized Network Total Cost of Operation . . . . . . . . 10
4.2. Optimized Decision Making . . . . . . . . . . . . . . . . 10
4.3. Safer Assessment of Innovative Network Capabilities . . . 10
4.4. Privacy and Regulatory Compliance . . . . . . . . . . . . 11
4.5. Customized Network Operation Training . . . . . . . . . . 11
5. Challenges to Build Digital Twin Network . . . . . . . . . . 11
6. A Reference Architecture of Digital Twin Network . . . . . . 13
7. Enabling Technologies to Build Digital Twin Network . . . . . 16
7.1. Data Collection and Data Services . . . . . . . . . . . . 16
7.2. Network Modeling . . . . . . . . . . . . . . . . . . . . 17
7.3. Network Visualization . . . . . . . . . . . . . . . . . . 18
7.4. Interfaces . . . . . . . . . . . . . . . . . . . . . . . 19
8. Interaction with IBN . . . . . . . . . . . . . . . . . . . . 19
9. Sample Application Scenarios . . . . . . . . . . . . . . . . 20
9.1. Human Training . . . . . . . . . . . . . . . . . . . . . 20
9.2. Machine Learning Training . . . . . . . . . . . . . . . . 20
9.3. DevOps-Oriented Certification . . . . . . . . . . . . . . 21
9.4. Network Fuzzing . . . . . . . . . . . . . . . . . . . . . 21
9.5. Digital Asset Management . . . . . . . . . . . . . . . . 21
10. Research Perspectives: A Summary . . . . . . . . . . . . . . 22
11. Security Considerations . . . . . . . . . . . . . . . . . . . 22
12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 23
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 23
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14. Open issues . . . . . . . . . . . . . . . . . . . . . . . . . 23
15. References . . . . . . . . . . . . . . . . . . . . . . . . . 23
15.1. Normative References . . . . . . . . . . . . . . . . . . 23
15.2. Informative References . . . . . . . . . . . . . . . . . 24
Appendix A. Change Logs . . . . . . . . . . . . . . . . . . . . 28
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 29
1. Introduction
The fast growth of network scale and the increased demand placed on
these networks require them to accommodate and adapt dynamically to
customer needs, implying a significant challenge to network
operators. Indeed, network operation and maintenance are becoming
more complex due to higher complexity of the managed networks and the
sophisticated services they are delivering. As such, providing
innovations on network technologies, management and operation will be
more and more challenging due to the high risk of interfering with
existing services and the higher trial costs if no reliable emulation
platforms are available.
A Digital Twin is the real-time representation of a physical entity
in the digital world. It has the characteristics of virtual-reality
interrelation and real-time interaction, iterative operation and
process optimization, full life-cycle and comprehensive data-driven
network infrastructure. Currently, digital twin has been widely
acknowledged in academic publications. See more in Section 3.
A digital twin for networks platform can be built by applying Digital
Twin technologies to networks and creating a virtual image of
physical network facilities (called herein, emulation). Basically,
the digital twin for networks is an expansion platform of network
simulation. The main difference compared to traditional network
management systems is the interactive virtual-real mapping and data
driven approach to build closed-loop network automation. Therefore,
a digital twin network platform is more than an emulation platform or
network simulator.
Through the real-time data interaction between the physical network
and its twin network(s), the digital twin network platform might help
the network designers to achieve more simplification, automatic,
resilient, and full life-cycle operation and maintenance. More
specifically, the digital twin network can, thus, be used to develop
various rich network applications and assess specific behaviors
(including network transformation) before actual implementation in
the physical network, tweak the network for better optimized
behavior, run 'what-if' scenarios that cannot be tested and evaluated
easily in the physical network. In addition, service impact analysis
tasks can also be facilitated.
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2. Terminology
2.1. Acronyms & Abbreviations
IBN: Intent-Based Networking
AI Artificial Intelligence
CI/CD: Continuous Integration/Continuous Delivery
ML: Machine Learning
OAM: Operations, Administration, and Maintenance
PLM: Product Lifecycle Management
2.2. Definitions
This document makes use of the following terms:
Digital Twin: a virtual instance of a physical system (twin) that is
continually updated with the latter's performance, maintenance,
and health status data throughout the physical system's life
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
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successfully applied in the fields of intelligent manufacturing,
smart city, or complex system operation and maintenance to help with
not only object design and testing, but also management aspects
[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 of systems are automatically
represented. This behavior is unlike a 'digital model' or 'digital
shadow', which are usually synchronized manually, lacking of control
data, and might not have a full cycle of data integrated.
At present (2022), there is no unified definition of digital twin
framework. The industry, scientific research institutions, and
standards developing organizations are trying to define a general or
domain-specific framework of digital twin. [Natis-Gartner2017]
proposed that building a digital twin of a physical entity requires
four key elements: model, data, monitoring, and uniqueness.
[Tao2019] proposed a five-dimensional framework of digital twin {PE,
VE, SS, DD, CN}, in which PE represents physical entity, VE
represents virtual entity, SS represents service, DD represents twin
data, and CN represents the connection between various components.
[ISO-2021] issued a draft standard for digital twin manufacturing
system, and proposed a reference framework including data collection
domain, device control domain, digital twin domain, and user domain.
3.2. Digital Twin for Networks
Communication networks provide a solid foundation for implementing
various 'digital twin' applications. At the same time, in the face
of increasing business types, scale and complexity, a network itself
also needs to use digital twin technology to seek enhanced and
optimized solutions compared to relying solely on the physical
network. The motivation for digital twin network can somehow be
traced back to some earlier concepts, such as "shadow MIB", inductive
modeling techniques, parallel systems, etc. Since 2017, the
application of digital twin technology in the field of communication
networks has gradually been researched as illustrated by the (non-
exhaustive) list of examples that are listed hereafter.
Within academia, [Dong2019] established the digital twin of 5G mobile
edge computing (MEC) network, used the twin offline to train the
resource allocation optimization and normalized energy-saving
algorithm based on reinforcement learning, and then updated the
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scheme to MEC network. [Dai2020] established a digital twin edge
network for mobile edge computing system, in which a twin edge server
is used to evaluate the state of entity server, and the twin mobile
edge computing system provides data for training offloading strategy.
[Nguyen2021] discusses how to deploy a digital twin for complex 5G
networks. [Hong2021] presents a digital twin platform towards
automatic and intelligent management for data center networks, and
then proposes a simplified the workflows of network service
management. In addition, international workshops dedicated to
digital twin in networking field have already appeared, such as IEEE
DTPI 2021 - Digital Twin Network Online Session [DTPI2021], and IEEE
NOMS 2022 - TNT workshop [TNT2022].
Although the application of digital twin technology in networking has
started, the research of digital twin for networks technology is
still in its infancy. Current applications focus on specific
scenarios (such as network optimization), where network digital twin
is just used as a network simulation tool to solve the problem of
network operation and maintenance. Combined with the characteristics
of digital twin technology and its application in other industries,
this document believes that digital twin network can be regarded as
an indispensable part of the overall network system and provides a
general architecture involving the whole life cycle of physical
network in the future, serving the application of network innovative
technologies such as network planning, construction, maintenance and
optimization, improving the automation and intelligence level of the
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.
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+-------------+ +--------------+
| | | |
| Mapping | | Interface |
| | | |
+-------------+-----------------+--------------+
| |
| Analyze, Diagnose |
| |
| +----------------------+ |
| | Digital Twin Network | |
| +----------------------+ |
+------------+ +------------+
| | Emulate, Control | |
| Models | | Data |
| |------------------------| |
+------------+ +------------+
Figure 1: Key Elements of Digital Twin Network
Data: A digital twin network should maintain historical data and/or
real time data (configuration data, operational state data,
topology data, trace data, metric data, process data, etc.) about
its real-world twin (i.e. physical network) that are required by
the models to represent and understand the states and behaviors of
the real-world twin.
The data is characterized as the single source of "truth" and
populated in the data repository, which provides timely and
accurate data service support for building various models.
Models: Techniques that involve collecting data from one or more
sources in the real-world twin and developing a comprehensive
representation of the data (e.g., system, entity, process) using
specific models. These models are used as emulation and diagnosis
basis to provide dynamics and elements on how the live physical
network operates and generates reasoning data utilized for
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.
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* 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.
Mapping: Used to identify the digital twin and the underlying
entities and establish a real-time interactive relation between
the physical network and the twin network or between two twin
networks. The mapping can be:
* One to one (pairing, vertical): Synchronize between a physical
network and its virtual twin network with continuous flows.
* One to many (coupling, horizontal): Synchronize among virtual
twin networks with occasional data exchange.
Such mappings provide a good visibility of actual status, making
the digital twin suitable to analyze and understand what is going
on in the physical network. It also allows using the digital twin
to optimize the performance and maintenance of the physical
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.
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Note: Real-time interaction is not always mandatory for all twins.
When testing some configuration changes or trying some innovative
techniques, the digital twins can behave as a simulation platform
without the need of real time telemetry data. And even in this
scenario, it is better to have interactive mapping capability so that
the validated changes can be tested in real network whenever required
by the testers. In most other cases (e.g., network optimization,
network fault recovery), real-time interaction between virtual and
real network is mandatory. This way, digital twin network can help
achieve the goal of autonomous network or self-driven network.
4. Benefits of Digital Twin Network
Digital twin network can help enabling closed-loop network management
across the entire lifecycle, from deployment and emulation, to
visualized assessment, physical deployment, and continuous
verification. By doing so, network operators and end-users to some
extent, as allowed by specific application interfaces, can maintain a
global, systemic, and consistent view of the network. Also, network
operators and/or enterprise user can safely exercise the enforcement
of network planning policies, deployment procedures, etc., without
jeopardizing the daily operation of the physical network.
The main difference between digital twin network and simulation
platform is the use of interactive virtual-real mapping to build
closed-loop network automation. Simulation platforms are the
predecessor of the digital twin network, one example of such a
simulation platform is network simulator [NS-3], which can be seen as
a variant of digital twin network but with low fidelity and lacking
for interactive interfaces to the real network. Compared with those
classical approaches, key benefits of digital twin network can be
summarized as follows:
1) Using real-time data to establish high fidelity twins, the
effectiveness of network simulation is higher; then the
simulation cost will be relatively low.
2) The impact and risk on running networks is low when automatically
applying configuration/policy changes after the full analysis and
required verifications (e.g., service impact analysis) within the
twin network.
3) The faults of the physical network can be automatically captured
by analyzing real-time data, then the correction strategy can be
distributed to the physical network elements after conducting
adequate analysis within the twins to complete the closed-loop
automatic fault repair.
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The following subsections further elaborate such benefits in details.
4.1. Optimized Network Total Cost of Operation
Large scale networks are complex to operate. Since there is no
effective platform for simulation, network optimization designs have
to be tested on the physical network at the cost of jeopardizing its
daily operation and possibly degrading the quality of the services
supported by the network. Such assessment greatly increases network
operator's Operational Expenditure (OPEX) budgets too.
With a digital twin network platform, network operators can safely
emulate candidate optimization solutions before deploying them in the
physical network. In addition, operator's OPEX on the real physical
network deployment will be greatly decreased accordingly at the cost
of the complexity of the assessment and the resources involved.
4.2. Optimized Decision Making
Traditional network operation and management mainly focus on
deploying and managing running services, but hardly support
predictive maintenance techniques.
Digital twin network can combine data acquisition, big data
processing, and AI modeling to assess the status of the network, but
also to predict future trends, and better organize predictive
maintenance. The ability to reproduce network behaviors under
various conditions facilitates the corresponding assessment of the
various evolution options as often as required.
4.3. Safer Assessment of Innovative Network Capabilities
Testing a new feature in an operational network is not only complex,
but also extremely risky. Service impact analysis is required to be
adequately achieved prior to effective activation of a new feature.
Digital twin network can greatly help assessing innovative network
capabilities without jeopardizing the daily operation of the physical
network. In addition, it helps researchers to explore network
innovation (e.g., new network protocols, network AI/ML applications)
efficiently, and network operators to deploy new technologies quickly
with lower risks. Take AI/ ML application as example, it is a
conflict between the continuous high reliability requirement (i.e.,
99.999%) and the slow learning speed or phase-in learning steps of
AI/ML algorithms. With digital twin network, AI/ML can complete the
learning and training with the sufficient data before deploying the
model in the real network. This would encourage more network AI
innovations in future networks.
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4.4. Privacy and Regulatory Compliance
The requirements on data confidentiality and privacy on network
providers increase the complexity of network management, as decisions
made by computation logics such as an SDN controller may rely upon
the packet payloads. As a result, the improvement of data-driven
management requires complementary techniques that can provide a
strict control based upon security mechanisms to guarantee data
privacy protection and regulatory compliance. This may range from
flow identification (using the archetypal five-tuple of addresses,
ports and protocol) to techniques requiring some degree of payload
inspection, all of them considered suitable to be associated to an
individual person, and hence requiring strong protection and/or data
anonymization mechanisms.
With strong modeling capability provided by the digital twin network,
very limited real data (if at all) will be needed to achieve similar
or even higher level of data-driven intelligent analysis. This way,
a lower demand of sensitive data will permit to satisfy privacy
requirements and simplify the use of privacy-preserving techniques
for data-driven operation.
4.5. Customized Network Operation Training
Network architectures can be complex, and their operation requires
expert personnel. Digital twin network offers an opportunity to
train staff for customized networks and specific user needs. Two
salient examples are the application of new network architectures and
protocols or the use of "cyber-ranges" to train security experts in
threat detection and mitigation.
5. Challenges to Build Digital Twin Network
According to [Hu2021], the main challenges in building and
maintaining digital twins can be summarized as the following five
aspects:
* Data acquisition and processing
* High-fidelity modeling
* Real-time, two-way communication between the virtual and the real
twins
* Unified development platform and tools
* Environmental coupling technologies
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Compared with other industrial fields, digital twin in networking
field has its unique characteristics. On one hand, network elements
and system have higher level of digitalization, which implies that
data acquisition and virtual-real communication are relatively easy
to achieve. On the other hand, there are various different type of
network elements and typologies in the network field; and the network
size is characterized by the numbers of nodes and links in it but the
network size growth pace can not meet the service needs, especially
in the deployment of end to end service which spans across multiple
administrative domains. So, the construction of a digital twin
network system needs to consider the following major challenges:
Large scale challenge: A digital twin of large-scale networks will
significantly increase the complexity of data acquisition and
storage, the design and implementation of relevant models. The
requirements of software and hardware of the digital twin network
system will be even more constraining. Therefore, efficient and
low cost tools in various fields should be required. Take data as
an example, massive network data can help achieve more accurate
models. However, the cost of virtual-real communication and data
storage becomes extremely expensive, especially in the multi-
domain data-driven network management case, therefore efficient
tools on data collection and data compression methods must be
used.
Interoperability: Due to the inconsistency of technical
implementations and the heterogeneity of vendor adopted
technologies, it is difficult to establish a unified digital twin
network system with a common technology in a network domain.
Therefore, it is needed firstly to propose a unified architecture
of digital twin network, in which all components and
functionalities are clear to all stakeholders; then define
standardized and unified interfaces to connect all network twins
via ensuring necessary compatibility.
Data modeling difficulties: Based on large-scale network data, data
modeling should not only focus on ensuring the accuracy of model
functions, but also has to consider the flexibility and
scalability to compose and extend as required to support large
scale and multi-purpose applications. Balancing these
requirements further increases the complexity of building
efficient and hierarchical functional data models. As an optional
solution, straightforwardly clone the real network using
virtualized resources is feasible to build the twin network when
the network scale is relatively small. However, it will be of
unaffordable resource cost for larger scales network. In this
case, network modeling using mathematical abstraction or
leveraging the AI algorithms will be more suitable solutions.
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Real-time requirements: Network services normally have real-time
requirements, the processing of model simulation and verification
through a digital twin network will introduce the service latency.
Meanwhile, the real-time requirements will further impose
performance requirements on the system software and hardware.
However, given the nature of distributed systems and propagation
delays, it is challenge to keep network digital twins in sync or
auto-sync between physical network and digital twin network.
Changes to the digital object automatically drive changes in the
physical object can be even challenging. To address these
requirements, the function and process of the data model need to
be based on automated processing mechanism under various network
application scenarios. On the one hand, it is needed to design a
simplified process to reduce the time cost for tasks in network
twin as much as possible; on the other hand, it is recommended to
define the real-time requirements of different applications, and
then match the corresponding computing resources and suitable
solutions as needed to complete the task processing in the twin.
Security risks: A digital twin network has to synchronize all or
subset of the data related to involved physical networks in real
time, which inevitably augments the attack surface, with a higher
risk of information leakage, in particular. On one hand, it is
mandatory to design more secure data mechanism leveraging legacy
data protection methods, as well as innovative technologies such
as block chain. On the other hand, the system design can limit
the data (especially raw data) requirement on building digital
twin network, leveraging innovative modeling technologies such as
federal learning.
In brief, to address the above listed challenges, it is important to
firstly propose a unified architecture of digital twin network, which
defines the main functional components and interfaces (Section 6).
Then, relying upon such an architecture, it is required to continue
researching on the key enabling technologies including data
acquisition, data storage, data modeling, interface standardization,
and security assurance.
6. A Reference Architecture of Digital Twin Network
Based on the definition of the key digital twin network technology
elements introduced in Section 3.3, a digital twin network
architecture is depicted in Figure 2. This digital twin network
architecture is broken down into three layers: Application Layer,
Digital Twin Layer, and Physical Network Layer.
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+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure| Intent Input
| |
+-------------+-------------------v-----------------------+
| Instance of Digital Twin Network |
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Network| |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^----------------------------+-------------------+
| |
| data collection | control
+--------+----------------------------v-------------------+
| Physical Network |
| |
+---------------------------------------------------------+
Figure 2: Reference Architecture of Digital Twin Network
Physical Network: All or subset of network elements in the physical
network exchange network data and control messages with a network
digital twin instance, through twin-physical control interfaces.
The physical network can be a mobile access network, a transport
network, a mobile core, a backbone, etc. The physical network can
also be a data center network, a campus enterprise network, an
industrial Internet of Things, etc.
The physical network can span across a single network
administrative domain or multiple network administrative domains.
This document focuses on the IETF related physical network such as
IP bearer network and data center network.
Digital Twin Layer: This layer includes three key subsystems: Data
Repository subsystem, Service Mapping Models subsystem, and
Digital Twin Network Management subsystem. These key subsystems
can be placed in one single network administrative domain and
provide the service to the application (e.g.,SDN controller) in
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other network administrative domain, or lied in every network
administrative domain and coordinate between each other to provide
services to the application in the upper layer.
One or multiple digital twin network instances can be built and
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'.
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* Digital Twin Network Management fulfils the management function
of digital twin network, records the life-cycle transactions of
the twin entity, monitors the performance and resource
consumption of the twin entity or even of individual models,
visualizes and controls various elements of the network digital
twin, including topology management, model management and
security management.
Notes: 'Data collection' and 'change control' are regarded as
southbound interfaces between virtual and physical network. From
implementation perspective, they can optionally form a sub-layer
or sub-system to provide common functionalities of data collection
and change control, enabled by a specific infrastructure
supporting bi-directional flows and facilitating data aggregation,
action translation, pre-processing and ontologies.
Application Layer: Various applications (e.g., Operations,
Administration, and Maintenance (OAM)) can effectively run over a
digital twin network platform to implement either conventional or
innovative network operations, with low cost and less service
impact on real networks. Network applications make requests that
need to be addressed by the digital twin network. Such requests
are exchanged through a northbound interface, so they are applied
by service emulation at the appropriate twin instance(s).
7. Enabling Technologies to Build Digital Twin Network
This section briefly describes several key enabling technologies to
build digital twin work system, based on the challenges and the
reference architecture described in above sections. Actually, each
enabling technology is worth of deep researching respectively and
separately.
7.1. Data Collection and Data Services
Data collection technology is the foundation of building data
repository for digital twin network. Target driven mode should be
adopted for data collection from heterogeneous data sources. The
type, frequency and method of data collection shall meet the
application of digital twin network. Whenever building network
models for a specific network application, the required data can be
efficiently obtained from the data repository.
Diverse existing tools and methods (e.g., SNMP, NETCONF [RFC6241],
IPFIX [RFC7011], telemetry [RFC9232]) can be used to collect
different type of network data. YANG data models and associated
mechanisms defined in [RFC8639][RFC8641] enable subscriber-specific
subscriptions to a publisher's event streams. Such mechanisms can be
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used by subscriber applications to request for a continuous and
customized stream of updates from a YANG datastore. Moreover, some
innovative methods (e.g., sketch-based measurement) can be used to
acquire more complex network data, such as network performance data.
Furthermore, data transformation and aggregation capabilities can be
used to improve the applicability on network modelling. Toward
building data repository for a digital twin system, data collection
tools and methods should be as lightweight as possible, so as to
reduce the volume of required network equipment resources, and
meaningful so it can be useful. Several solutions related to data
collection are work-in-progress in IETF/IRTF, e.g., adaptive
subscription [I-D.ietf-netconf-adaptive-subscription], efficient data
collection [I-D.zcz-nmrg-digitaltwin-data-collection], and contextual
information [I-D.claise-opsawg-collected-data-manifest].
Data repository works to effectively store large-scale and
heterogeneous network data, as well provide data and services to
build various network models. So, it is also necessary to study
technologies regarding data services including fast search, batch-
data handling, conflict avoidance, data access interfaces, etc.
7.2. Network Modeling
The basic network element models and topology models help generate
virtual twin of the network according to the network element
configuration, operation data, network topology relationship, link
state and other network information. Then the operation status can
be monitored and displayed, and the network configuration change and
optimization strategy can be pre-verified.
For small scale network, network simulating tools (e.g., [NS-3],
[Mininet], etc.) and emulating tools (e.g., [EVE-NG], [GNS-3]) can be
used to build basic network models. By using the packet processing
capability of virtual network element, such tools can quickly verify
the functions of the control plane and data plane. However, this
modeling method also has many limitations, including high resource
consumption, poor performance analysis ability, and poor scalability.
For large scale network, mathematical abstraction methods can be used
to build basic network models efficiently. Knowledge graph, network
calculus, and formal verification can be candidate methods. Some
relevant researches have emerged in recent years, such as [Hong2021],
[G2-SIGCOMM], and [DNA-2022]. Going forward, how to improve the
extensibility and accuracy of the models is still a big challenge.
As an example, the theory of bottleneck structures introduced in
[G2-SIGCOMM, G2-SIGMETRICS] can be used to construct a mathematical
model of the network (see also
[I-D.giraltyellamraju-alto-bsg-requirements] for more info). A
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bottleneck structure is a computational graph that efficiently
captures the topology, the routing and flow properties of the
network. The graph embeds the latent relationships that exist
between bottlenecks and the application flows in a distributed
system, providing an efficient mathematical framework to compute the
ripple effects of perturbations (e.g., a flow arriving or departing
from the system, or the dynamic change in capacity of a wireless
link, among others). Because these perturbations can be seen as
mathematical derivatives of the communication system, bottleneck
structures can be used to compute optimized network configurations,
providing a natural engineering sandbox for building network models.
One of the key advantages of bottleneck structures is that they can
be used to compute (symbolically or numerically) key performance
indicators of the network (e.g., expected flow throughput, projected
flow completion time, etc.) without the need to use computationally
intensive simulators. This capability can be especially useful when
building a digital twin or a large-scale network, potentially saving
orders or magnitude in computational resources in comparison to
simulation or emulation-based approaches.
The functional model aims to realize the dynamic evolution of network
performance evaluation and intelligent decision-making. Data driven
AI/ML algorithm will play a great role in building complex network
functional models. As a research hotspot in recent years, many
successfully cases have been demonstrated, such as [RouteNet],
[MimicNet], etc. In the future, in addition to improving the
generalization ability and interpretability of AI models, we also
need to focus on how to improve the real-time and interactivity of
model reasoning based on data and control in network digital twin
layer.
7.3. Network Visualization
It is the internal requirement of the digital twin network system to
use network visibility technology to visually present the data and
model in the network twin with high fidelity and intuitively reflect
the interactive mapping between the physical network entity and the
network twin. Network Visibility technology can help users understand
the internal structure of the network, and also help mine valuable
information hidden in the network.
Network Visibility can use algorithms such as hierarchical layout,
heuristic layout or force oriented layout (or a combination of
several algorithms) for topology layout. And the related topology
data can be acquired using solutions provided in [RFC8345],
[RFC8346], [RFC8944], etc. Meanwhile, digital twin network system
can select different interaction methods or combinations of
interaction methods to realize the visual dynamic interaction mapping
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of virtual and real networks. The data query technology, such as
SPARQL, can be used to express queries across diverse data sources,
whether the data is stored natively as RDF or viewed as RDF via
middleware.
7.4. Interfaces
Based on the reference architecture, there are three types of
interfaces on building a digital twin network system.
1) Network-facing interfaces are twin interfaces between the
physical network and its twin entity. They are responsible for
information exchange between physical network and network digital
twin. The candidate interfaces can be SNMP, NETCONF, etc.
2) Application-facing interfaces are Application-facing interfaces
between the network digital twin and applications. They are
responsible for information exchange between network digital twin
and network applications. The lightweight and extensible
[RESTFul] interface can be the candidate northbound interface.
3) Internal interfaces are within network digital twin layer. They
are responsible for information exchange between the three
subsystems: Data Repository, Service Mapping Models, and Digital
Twin Network Management. These interfaces should be of high-
speed, high-efficiency and high-concurrency. The candidate
interfaces or protocols can be XMPP (defined in [RFC7622]), and
HTTP/3.0 (defined in [RFC9114]).
All interfaces are recommended to be open and standardized so as to
help avoid either hardware or software vendor lock, and achieve
interoperability. Besides the interfaces list above, some new
interfaces or protocols can be created to better serve digital twin
network system.
8. Interaction with IBN
Implementing Intent-Based Networking (IBN) is an innovative
technology for life-cycle network management. Future networks will
be possibly Intent-based, which means that users can input their
abstract 'intent' to the network, instead of detailed policies or
configurations on the network devices. [RFC9315] clarifies the
concept of "Intent" and provides an overview of IBN functionalities.
The key characteristic of an IBN system is that user intent can be
assured automatically via continuously adjusting the policies and
validating the real-time situation.
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IBN can be envisaged in a digital twin network context to show how
digital twin network improves the efficiency of deploying network
innovation. To lower the impact on real networks, several rounds of
adjustment and validation can be emulated on the digital twin network
platform instead of directly on physical network. Therefore, the
digital twin network can be an important enabler platform to
implement IBN systems and fooster their deployment.
9. Sample Application Scenarios
Digital twin network can be applied to solve different problems in
network management and operation.
9.1. Human Training
The usual approach to network OAM with procedures applied by humans
is open to errors in all these procedures, with impact in network
availability and resilience. Response procedures and actions for
most relevant operational requests and incidents are commonly defined
to reduce errors to a minimum. The progressive automation of these
procedures, such as predictive control or closed-loop management,
reduce the faults and response time, but still there is the need of a
human-in-the-loop for multiples actions. These processes are not
intuitive and require training to learn how to respond.
The use of digital twin network for this purpose in different network
management activities will improve the operators performance. One
common example is cybersecurity incident handling, where "cyber-
range" exercises are executed periodically to train security
practitioners. Digital twin network will offer realistic
environments, fitted to the real production networks.
9.2. Machine Learning Training
Machine Learning requires data and their context to be available in
order to apply it. A common approach in the network management
environment has been to simulate or import data in a specific
environment (the ML developer lab), where they are used to train the
selected model, while later, when the model is deployed in
production, re-train or adjust to the production environment context.
This demands a specific adaption period.
Digital twin network simplifies the complete ML lifecycle development
by providing a realistic environment, including network topologies,
to generate the data required in a well-aligned context. Dataset
generated belongs to the digital twin network and not to the
production network, allowing information access by third parties,
without impacting data privacy.
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9.3. DevOps-Oriented Certification
The potential application of CI/CD models network management
operations increases the risk associated to deployment of non-
validated updates, what conflicts with the goal of the certification
requirements applied by network service providers. A solution for
addressing these certification requirements is to verify the specific
impacts of updates on service assurance and Service Level Agreements
(SLAs) using a digital twin network environment replicating the
network particularities, as a previous step to production release.
Digital twin network control functional block supports such dynamic
mechanisms required by DevOps procedures.
9.4. Network Fuzzing
Network management dependency on programmability increases systems
complexity. The behavior of new protocol stacks, API parameters, and
interactions among complex software components are examples that
imply higher risk to errors or vulnerabilities in software and
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.
9.5. Digital Asset Management
With the development of enterprise digitization, the number of
enterprise Internet of Objects (IoT) devices, virtualized Cloud
assets (e.g., virtual firewall), and network asset (e.g., switches,
routers) also increases. The endpoints connected to an enterprise
network lack coherent modelling and lifecycle management because
different services are modelled, collected, processed, and stored
separately. The same category of network devices (including network
endpoints) may be repeatedly discovered, processed, and stored.
Therefore, the inventory is difficult to manage when they are tracked
in different places without formal synchronization procedures.
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Digital twin management can be used as a means to ensure consistent
representation and reporting of asset types. In doing so, the
enforcement of security policies and assessment will be further
simplified. Such an approach will ease implementing a unified
control strategy for all assets types connected to an enterprise
network. It also make actors on assets more accountable for
breaching their compliance promises. Special care should be
considerd to protext the inventory data since it may be gather
privacy-senstive information.
The digital asset management for twins can be used, for example, to
exercise the implication of End of Life (EoL), dependency, and
hardware dependency "what-if" scenarios.
10. Research Perspectives: A Summary
Research on digital twin network has just started. This document
presents an overview of the digital twin network concepts and
reference architecture. Looking forward, further elaboration on
digital twin network scenarios, requirements, architecture, and key
enabling technologies should be investigated by the industry, so as
to accelerate the implementation and deployment of digital twin
network.
11. Security Considerations
This document describes concepts and definitions of digital twin
network. As such, the following security considerations remain high
level, i.e., in the form of principles, guidelines or requirements.
Security considerations of the digital twin network include:
* Secure the digital twin system itself.
* Data privacy protection.
Securing the digital twin network system aims at making the digital
twin system operationally secure by implementing security mechanisms
and applying security best practices. In the context of digital twin
network, such mechanisms and practices may consist in data
verification and model validation, mapping operations between
physical network and digital counterpart network by authenticated and
authorized users only.
Synchronizing the data between the physical and the digital twin
networks may increase the risk of sensitive data and information
leakage. Strict control and security mechanisms must be provided and
enabled to prevent data leaks.
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12. Acknowledgements
Many thanks to the NMRG participants for their comments and reviews.
Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia, Jerome
Francois, Jordi Paillisse, Luis Miguel Contreras Murillo, Alexander
Clemm, Qiao Xiang, Ramin Sadre, Pedro Martinez-Julia, Wei Wang,
Zongpeng Du, and Peng Liu.
Diego Lopez and Antonio Pastor were partly supported by the European
Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
and grant agreement no. 871808 (INSPIRE-5Gplus).
13. IANA Considerations
This document has no requests to IANA.
14. Open issues
* Some technologies (e.g. Network connectivity, Real-time data
communication, Collaboration management, conflict detection and
resolution, etc.) recently discussed in the IRTF/IETF should be
described.
* In section of 'Sample Application Scenarios', to dig deeper into
one or two use cases.
15. References
15.1. Normative References
[RFC7622] Saint-Andre, P., "Extensible Messaging and Presence
Protocol (XMPP): Address Format", RFC 7622,
DOI 10.17487/RFC7622, September 2015,
<https://www.rfc-editor.org/info/rfc7622>.
[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, <https://www.rfc-editor.org/info/rfc8345>.
[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, <https://www.rfc-editor.org/info/rfc8346>.
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[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/info/rfc8639>.
[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/info/rfc8641>.
[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,
<https://www.rfc-editor.org/info/rfc8944>.
[RFC9114] Bishop, M., Ed., "HTTP/3", RFC 9114, DOI 10.17487/RFC9114,
June 2022, <https://www.rfc-editor.org/info/rfc9114>.
15.2. Informative References
[Dai2020] Dai, Y. Dai., Zhang, K. Zhang., Maharjan, S. Maharjan.,
and Yan Zhang. Zhang, "Deep Reinforcement Learning for
Stochastic Computation Offloading in Digital Twin
Networks. IEEE Transactions on Industrial Informatics,
vol. 17, no. 17", August 2020.
[DNA-2022] Zhang, P. Zhang., Gember-Jacobson, A. Gember-Jacobson.,
Zuo, Y. Zuo., Huang, Y Huang., Liu, X. Liu., and H. Li.
Li, "Differential Network Analysis, USENIX Symposium on
Networked Systems Design and Implementation (NSDI 22)",
April 2022.
[Dong2019] Dong, R. Dong., She, C. She., HardjawanaLiu, W.
Hardjawana., Li, Y. Li., and B. Vucetic. Vucetic, "Deep
Learning for Hybrid 5G Services in Mobile Edge Computing
Systems: Learn from a Digital Twin. IEEE Transactions on
Wireless Communications,vol. 18, no. 10", July 2019.
[DTPI2021] "IEEE International Conference on Digital Twins and
Parallel Intelligence - Digital Twin Network Session,
https://www.dtpi.org/video/10", July 2021.
[EVE-NG] "Emulated Virtual Environment Next Generation, EVE-NG.
https://www.eve-ng.net/".
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[Fuller2020]
Fuller, A. Fuller., Fan, Z., Day, C., and C. Barlow,
"Digital Twin: Enabling Technologies, Challenges and Open
Research," in IEEE Access, vol. 8, pp. 108952-108971",
2020.
[G2-SIGCOMM]
Ros-Giralt, J. Ros-Giralt., Amsel, N. Amsel., Yellamraju,
S. Yellamraju., Ezick, J. Ezick., Lethin, R. Lethin.,
Jiang, Y. Jiang., Feng, A. Feng., Tassiulas, L.
Tassiulas., Wu, Z. Wu., and K, Bergman. Bergman,
"Designing data center networks using bottleneck
structures", ACM SIGCOMM", August 2021.
[G2-SIGMETRICS]
Ros-Giralt, J. Ros-Giralt., Bohara, A. Bohara.,
Yellamraju, S. Yellamraju., Langston, H. Langston.,
Lethin, R. Lethin., Jiang, Y. Jiang., Tassiulas, L.
Tassiulas., Li, J. Li., Tan, Y. Tan., and M.
Veeraraghavan. Veeraraghavan, "On the Bottleneck Structure
of Congestion-Controlled Networks, ACM SIGMETRICS",
December 2019.
[GNS3] "Graphical Network Simulator-3, GNS3.
https://www.gns3.com/".
[Grieves2014]
Grieves, M. Grieves., "Digital twin: Manufacturing
excellence through virtual factory replication", 2003,
<https://www.3ds.com/fileadmin/PRODUCTS-
SERVICES/DELMIA/PDF/Whitepaper/DELMIA-APRISO-Digital-Twin-
Whitepaper.pdf>.
[Hong2021] Hong, H., Wu, Q., Dong, F., Song, W., Sun, R., Han, T.,
Zhou, C., and H. Yang, "NetGraph: An Intelligent Operated
Digital Twin Platform for Data Center Networks. In ACM
SIGCOMM 2021 Workshop on Network-Application Integration
(NAI' 21), Virtual Event, USA. ACM, New York, NY, USA",
2021.
[Hu2021] Hu, W., Zhang, T., Deng, X., Liu, Z., and J. Tan, "Digital
twin: a state-of-the-art review of its enabling
technologies, applications and challenges. Journal of
Intelligent Manufacturing and Special Equipment, Vol. 2
No. 1, pp. 1-34", 2021.
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[I-D.claise-opsawg-collected-data-manifest]
Claise, B., Quilbeuf, J., Diego Lopez, R., Dominguez, I.,
and T. Graf, "A Data Manifest for Contextualized Telemetry
Data", Work in Progress, Internet-Draft, draft-claise-
opsawg-collected-data-manifest-04, 25 July 2022,
<https://www.ietf.org/archive/id/draft-claise-opsawg-
collected-data-manifest-04.txt>.
[I-D.giraltyellamraju-alto-bsg-requirements]
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,
<https://www.ietf.org/archive/id/draft-giraltyellamraju-
alto-bsg-requirements-03.txt>.
[I-D.ietf-netconf-adaptive-subscription]
Wu, Q., Song, W., Liu, P., Ma, Q., Wang, W., and Z. Niu,
"Adaptive Subscription to YANG Notification", Work in
Progress, Internet-Draft, draft-ietf-netconf-adaptive-
subscription-01, 21 October 2022,
<https://www.ietf.org/archive/id/draft-ietf-netconf-
adaptive-subscription-01.txt>.
[I-D.zcz-nmrg-digitaltwin-data-collection]
Zhou, C., Chen, D., and P. Martinez-Julia, "Data
Collection Requirements and Technologies for Digital Twin
Network", Work in Progress, Internet-Draft, draft-zcz-
nmrg-digitaltwin-data-collection-00, 10 July 2022,
<https://www.ietf.org/archive/id/draft-zcz-nmrg-
digitaltwin-data-collection-00.txt>.
[ISO-2021] ISO, "Digital Twin manufacturing framework - Part 2:
Reference architecture: ISO/CD 23247-2.
https://www.iso.org/standard/78743.html", 2021.
[Madni2019]
Madni, A. Madni., Madni, C. Madni., and S. Lucero. Lucero,
"Leveraging digital twin technology in model-based systems
engineering. Systems, vol. 7, no. 1, p. 7", January 2019.
[MimicNet] Zhang, Q. Zhang., NG, K. K.W. NG., Kazer, C. W. Kazer.,
Yan, S. Yan., Sedoc, J. Sedoc., and V. Liu. Liu,
"MimicNet: Fast Performance Estimates for Data Center
Networks with Machine Learning. In ACM SIGCOMM 2021
Conference (SIGCOMM ’21).", August 2021.
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[Mininet] "Mninet: An Instant Virtual Network on your Laptop,
http://mininet.org/".
[Natis-Gartner2017]
Natis, Y. Natis., Velosa, A. Velosa., and W. R. Schulte.
Schulte, "Innovation insight for digital twins - driving
better IoT-fueled decisions.
https://www.gartner.com/en/documents/3645341", 2017.
[Nguyen2021]
Nguyen, H. X. Nguyen., Trestian, R. Trestian., To, D. To.,
and M. Tatipamula. Tatipamula, "Digital Twin for 5G and
Beyond. IEEE Communications Magazine, vol. 59, no. 2",
February 2021.
[NS-3] "Network Simulator, NS-3. https://www.nsnam.org/".
[RESTFul] Richardson, L. Richardson. and M. Amundsen. Amundsen,
"RESTful Web APIs. O'Reilly Media, Inc.", 2013.
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[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,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC9232] Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
A. Wang, "Network Telemetry Framework", RFC 9232,
DOI 10.17487/RFC9232, May 2022,
<https://www.rfc-editor.org/info/rfc9232>.
[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, <https://www.rfc-editor.org/info/rfc9315>.
[Roson2015]
Rosen, R. Rosen., Wichert, G. Von Wichert., Lo, G. Lo.,
and K.D. Bettenhausen. Bettenhausen, "About the importance
of autonomy and DTs for the future of manufacturing. IFAC-
Papersonline, Vol. 48, pp. 567-572.", 2015.
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[RouteNet] Rusek, K. Rusek., Suárez-Varela, J. Suárez-Varela.,
Almasan, P. Almasan., Barlet-Ros, P. Barlet-Ros., and A.
Cabellos-Aparicio. Cabellos-Aparicio, "RouteNet:
Leveraging Graph Neural Networks for network modeling and
optimization in SDN. IEEE Journal on Selected Areas in
Communication (JSAC), vol. 38, no. 10", October 2020.
[Tao2019] Tao, F. Tao., Zhang, H. Zhang., Liu, A. Liu., and A. Y. C.
Nee. Nee, "Digital Twin in Industry: State-of-the-Art.
IEEE Transactions on Industrial Informatics, vol. 15, no.
4.", April 2019.
[TNT2022] "IEEE International workshop on Technologies for Network
Twins, https://noms2022.ieee-noms.org/ws4-1st-
international-workshop-technologies-network-twins-tnt-
2022", 2022.
Appendix A. Change Logs
v06 - v07: Addressed reviewer's comments from adoption call,
including below major changes.
* Resequenced the sections via adding more subsections on concepts
of digital twin network, removing the 'Requirements Language'
section, and moving ahead the 'Challenges' section.
* Cited more papers, or industrial information on digital twin
concepts and digital twin for networks.
* Added more information on describing the challenges and key
characteristics digital twin network.
* Removed previous open issue on investigating related digital twin
network work and identify the differences and commonalities, and
added several new open issues for future study.
* Other editorial changes.
v05 - v06: Addressed comments form meeting and maillist, to request
adoptoin call.
* Remove acronym DTN to avoid conflict with 'Delay Tolerant
Network';
* Elaborate the descriptoin of Digital Twin Network architecture
that supports multiple instances;
* Other Editorial changes.
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04 - v05
* Clarify the difference between digital twin network platform and
traditional network management system;
* Add more references of researches on applying digital twin to
network field;
* Clarify the benefit of 'Privacy and Regulatory Compliance';
* Refine the description of reference architecture;
* Other Editorial changes.
v03 - v04
* Update data definition and models definitions to clarify their
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
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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
Qin Wu
Huawei
101 Software Avenue, Yuhua District
Nanjing
Jiangsu, 210012
China
Email: bill.wu@huawei.com
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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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