Internet DRAFT - draft-zhou-nmrg-digitaltwin-network-concepts
draft-zhou-nmrg-digitaltwin-network-concepts
Internet Research Task Force C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: 6 September 2022 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
5 March 2022
Digital Twin Network: Concepts and Reference Architecture
draft-zhou-nmrg-digitaltwin-network-concepts-07
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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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Acronyms & Abbreviations . . . . . . . . . . . . . . . . 3
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. Interaction with IBN . . . . . . . . . . . . . . . . . . . . 16
8. Sample Application Scenarios . . . . . . . . . . . . . . . . 17
8.1. Human Training . . . . . . . . . . . . . . . . . . . . . 17
8.2. Machine Learning Training . . . . . . . . . . . . . . . . 17
8.3. DevOps-Oriented Certification . . . . . . . . . . . . . . 18
8.4. Network Fuzzing . . . . . . . . . . . . . . . . . . . . . 18
9. Research Perspectives: A Summary . . . . . . . . . . . . . . 18
10. Security Considerations . . . . . . . . . . . . . . . . . . . 18
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 19
12. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
13. Open issues . . . . . . . . . . . . . . . . . . . . . . . . . 19
14. Informative References . . . . . . . . . . . . . . . . . . . 20
Appendix A. Change Logs . . . . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 23
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1. Introduction
The fast growth of network scale and the increased demand placed on
these networks require them to accommodate and adapt dynamically to
customer needs, implying a significant challenge to network
operators. Indeed, network operation and maintenance are becoming
more complex due to higher complexity of the managed networks and the
sophisticated services they are delivering. As such, providing
innovations on network technologies, management and operation will be
more and more challenging due to the high risk of interfering with
existing services and the higher trial costs if no reliable emulation
platforms are available.
A Digital Twin is the real-time representation of a physical entity
in the digital world. It has the characteristics of virtual-reality
interrelation and real-time interaction, iterative operation and
process optimization, full life-cycle and comprehensive data-driven
network infrastructure. Currently, digital twin has been widely
acknowledged in academic publications. See more in Section 3.
A digital twin for networks platform can be built by applying Digital
Twin technologies to networks and creating a virtual image of
physical network facilities (called herein, emulation). Basically,
the digital twin for networks is an expansion platform of network
simulation. The main difference compared to traditional network
management systems is the interactive virtual-real mapping and data
driven approach to build closed-loop network automation. Therefore,
a digital twin network platform is more than an emulation platform or
network simulator.
Through the real-time data interaction between the physical network
and its twin network(s), the digital twin network platform might help
the network designers to achieve more simplification, automatic,
resilient, and full life-cycle operation and maintenance. More
specifically, the digital twin network can, thus, be used to develop
various rich network applications and assess specific behaviors
(including network transformation) before actual implementation in
the physical network, tweak the network for better optimized
behavior, run 'what-if' scenarios that cannot be tested and evaluated
easily in the physical network. In addition, service impact analysis
tasks can also be facilitated.
2. Terminology
2.1. Acronyms & Abbreviations
IBN: Intent-Based Networking
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IA: 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
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].
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Compared with 'digital model' and 'digital shadow', the key
difference of 'digital twin' is the direction of data between the
physical and virtual systems [Fuller2020]. Typically, when using a
digital twin, the (twin) system is generated and then synchronized
using data flows in both directions between physical and digital
components, so that control data can be sent, and changes between the
physical and digital objectives and systems are automatically
represented. This behavior is unlike a 'digital model' or 'digital
shadow', which are usually synchronized manually, lacking of control
data, and might not have a full cycle of data integrated.
At present (2022), there is no unified definition of digital twin
framework. The industry, scientific research institutions, and
standards developing organizations are trying to define a general or
domain-specific framework of digital twin. [Natis-Gartner2017]
proposed that building a digital twin of a physical entity requires
four key elements: model, data, monitoring, and uniqueness.
[Tao2019] proposed a five-dimensional framework of digital twin {PE,
VE, SS, DD, CN}, in which PE represents physical entity, VE
represents virtual entity, SS represents service, DD represents twin
data, and CN represents the connection between various components.
[ISO-2021] issued a draft standard for digital twin manufacturing
system, and proposed a reference framework including data collection
domain, device control domain, digital twin domain, and user domain.
3.2. Digital Twin for Networks
Communication networks can provide a solid foundation for
implementing various 'digital twin' applications. At the same time,
in the face of increasing business types, scale and complexity, a
network itself also needs to use digital twin technology to seek
better solutions beyond physical network. Since 2017, the
application of digital twin technology in the field of communication
networks has gradually been researched. Some examples are listed
below.
In academy, [Dong2019] established the digital twin of 5G mobile edge
computing (MEC) network, used the twin offline to train the resource
allocation optimization and normalized energy-saving algorithm based
on reinforcement learning, and then updated the scheme to MEC
network. [Dai2020] established a digital twin edge network for
mobile edge computing system, in which a twin edge server is used to
evaluate the state of entity server, and the twin mobile edge
computing system provides data for training offloading strategy.
[Nguyen2021] discusses how to deploy a digital twin for complex 5G
networks. [Hong2021] presents a digital twin platform towards
automatic and intelligent management for data center networks, and
then proposes a simplified the workflows of network service
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management. In addition, international workshops dedicated to
digital twin in network field have already appeared, such as IEEE
DTPI 2021 - Digital Twin Network Online Session [DTPI2021], or are
being proposed such as IEEE NOMS 2022 - TNT workshop [TNT2022].
Although the application of digital twin technology in networking has
started, the research of digital twin for networks technology is
still in its infancy. Current applications focus on specific
scenarios (such as network optimization), where network digital twin
is just used as a network simulation tool to solve the problem of
network operation and maintenance. Combined with the characteristics
of digital twin technology and its application in other industries,
this document believes that digital twin network can be regarded as
an organic whole of the overall network system and become a general
architecture involving the whole life cycle of physical network in
the future, serving the application of network innovative
technologies such as network planning, construction, maintenance and
optimization, improving the automation and intelligence level of the
network.
3.3. Definition of Digital Twin Network
So far, there is no standard definition of "digital twin network"
within the networking industry. This document defines "digital twin
network" as a virtual representation of the physical network. Such
virtual representation of the network is meant to be used to analyze,
diagnose, emulate, and then control the physical network based on
data, models, and interfaces. To that aim, a real-time and
interactive mapping is required between the physical network and its
virtual twin network.
Referring the characteristics of digital twin in other industries and
the characteristics of the networking itself, the digital twin
network should involve four key elements: data, mapping, models and
interfaces as shown in Figure 1.
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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 mantaining
digital twins can be summarized as the following five aspects:
* Data acquisition and processing
* High-fidelity modeling
* Real-time, two-way connection between the virtual and the real
twins
* Unified development platform and tools
* Environmental coupling technologies
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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 connection are relatively easy to
achieve. On the other hand, there are many kinds of network elements
and topologies in the network field; and the complex giant system of
network carries a variety of business services. So, the construction
of a digital twin network system needs to consdier the following
major challenges:
Large scale challenge: A digital twin of large-scale networks will
significantly increase the complexity of data acquisition and
storage, the design and implementation of relevant models. The
requirements of software and hardware of the digital twin network
system will be even more constraining. Therefore, efficient and
low cost tools in various fields should be required. Take data as
an example, massive network data can help achieve more accurate
models. However, to lower the cost of virtual-real communication
and data storage, efficient tools on data collection and data
compression methods must be used.
Interoperability: Due to the inconsistency of technical
implementations and the heterogeneity of vendor technologies, it
is difficult to establish a unified digital twin network system
with a common technology in a network domain. Therefore, it is
needed firstly to propose a unified architecture of digital twin
network, in which all components and functionalities are clear to
all stakeholders; then define standardized and unified interfaces
to connect all network twins via ensuring necessary compatibility.
Data modeling difficulties: Based on large-scale network data, data
modeling should not only focus on ensuring the accuracy of model
functions, but also has to consider the flexibility and
scalability to compose and extend as required to support large
scale and multi-purpose applications. Balancing these
requirements further increases the complexity of building
efficient and hierarchical functional data models. As an optional
solution, straightforwardly clone the real network using
virtualized resources is feasible to build the twin network when
the network scale is relatively small. However, it will be of
unaffordable resource cost for larger scales network. In this
case, network modeling using mathematical abstraction or
leveraging the AI algorithms will be more suitable solutions.
Real-time requirements: Network services normally have real-time
requirements, the processing of model simulation and verification
through a digital twin network will increase the service latency.
Meanwhile, the real-time requirements will further increase
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performance requirements on the system software and hardware.
Moreover, it is also challenge to keep network digital twins in
sync given the nature of distributed systems and propagation
delays. To address these requirements, the function and process
of the data model need to be based on automated processing
mechanism under various network application scenarios. On the one
hand, it is needed to design a simplified process to reduce the
time cost for tasks in network twin as much as possible; on the
other hand, it is recommended to define the real-time requirements
of different applications, and then match the corresponding
computing resources and suitable solutions as needed to complete
the task processing in the twin.
Security risks: A digital twin network has to synchronize all or
subset of the data related to involved physical networks in real
time, which inevitably augments the attack surface, with a higher
risk of information leakage, in particular. On one hand, it is
mandatory to design more secure data mechanism leveraging legacy
data protection methods, as well as innovative technologies such
as block chain. On the other hand, the system design can limit
the data (especially raw data) requirement on building digital
twin network, leveraging innovative modeling technologies such as
federal learning.
In brief, to address the above listed challenges, it is important to
firstly propose a unified architecture of digital twin network, which
defines the main functional components and interfaces (Section 6).
Then, relying upon such an architecture, it is required to continue
researching on the key enabling technologies including data
acquisition, data storage, data modeling, interface standardization,
and security assurance.
6. A Reference Architecture of Digital Twin Network
Based on the definition of the key digital twin network technology
elements introduced in Section 3.3, a digital twin network
architecture is depicted in Figure 2. This digital twin network
architecture is broken down into three layers: Application Layer,
Digital Twin Layer, and Physical Network Layer.
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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 datacenter network.
Digital Twin Layer: This layer includes three key subsystems: Data
Repository subsystem, Service Mapping Models subsystem, and
Digital Twin Network Management subsystem.
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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. Interaction with IBN
Implementing Intent-Based Networking (IBN) is an innovative
technology for life-cycle network management. Future networks will
be possibly Intent-based, which means that users can input their
abstract 'intent' to the network, instead of detailed policies or
configurations on the network devices.
[I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the concept of
"Intent" and provides an overview of IBN functionalities. The key
characteristic of an IBN system is that user intent can be assured
automatically via continuously adjusting the policies and validating
the real-time situation.
IBN can be envisaged in a digital twin network context to show how
digital twin network improves the efficiency of deploying network
innovation. To lower the impact on real networks, several rounds of
adjustment and validation can be emulated on the digital twin network
platform instead of directly on physical network. Therefore, digital
twin network can be an important enabler platform to implement IBN
systems and speed up their deployment.
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8. Sample Application Scenarios
Digital twin network can be applied to solve different problems in
network management and operation.
8.1. Human Training
The usual approach to network OAM with procedures applied by humans
is open to errors in all these procedures, with impact in network
availability and resilience. Response procedures and actions for
most relevant operational requests and incidents are commonly defined
to reduce errors to a minimum. The progressive automation of these
procedures, such as predictive control or closed-loop management,
reduce the faults and response time, but still there is the need of a
human-in-the-loop for multiples actions. These processes are not
intuitive and require training to learn how to respond.
The use of digital twin network for this purpose in different network
management activities will improve the operators performance. One
common example is cybersecurity incident handling, where "cyber-
range" exercises are executed periodically to train security
practitioners. Digital twin network will offer realistic
environments, fitted to the real production networks.
8.2. Machine Learning Training
Machine Learning requires data and their context to be available in
order to apply it. A common approach in the network management
environment has been to simulate or import data in a specific
environment (the ML developer lab), where they are used to train the
selected model, while later, when the model is deployed in
production, re-train or adjust to the production environment context.
This demands a specific adaption period.
Digital twin network simplifies the complete ML lifecycle development
by providing a realistic environment, including network topologies,
to generate the data required in a well-aligned context. Dataset
generated belongs to the digital twin network and not to the
production network, allowing information access by third parties,
without impacting data privacy.
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8.3. DevOps-Oriented Certification
The potential application of CI/CD models network management
operations increases the risk associated to deployment of non-
validated updates, what conflicts with the goal of the certification
requirements applied by network service providers. A solution for
addressing these certification requirements is to verify the specific
impacts of updates on service assurance and SLAs using a digital twin
network environment replicating the network particularities, as a
previous step to production release.
Digital twin network control functional block supports such dynamic
mechanisms required by DevOps procedures.
8.4. Network Fuzzing
Network management dependency on programmability increases systems
complexity. The behavior of new protocol stacks, API parameters, and
interactions among complex software components are examples that
imply higher risk to errors or vulnerabilities in software and
configuration.
Digital twin network allows to apply fuzzing testing techniques on a
twin network environment, with interactions and conditions similar to
the production network, permitting to identify and solve
vulnerabilities, bugs and zero-days attacks before production
delivery.
9. Research Perspectives: A Summary
Research on digital twin network has just started. This document
presents an overview of the digital twin network concepts and
reference architecture. Looking forward, further elaboration on
digital twin network scenarios, requirements, architecture, and key
enabling technologies should be investigated by the industry, so as
to accelerate the implementation and deployment of digital twin
network.
10. Security Considerations
This document describes concepts and definitions of digital twin
network. As such, the following security considerations remain high
level, i.e., in the form of principles, guidelines or requirements.
Security considerations of the digital twin network include:
* Secure the digital twin system itself.
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* Data privacy protection.
Securing the digital twin network system aims at making the digital
twin system operationally secure by implementing security mechanisms
and applying security best practices. In the context of digital twin
network, such mechanisms and practices may consist in data
verification and model validation, mapping operations between
physical network and digital counterpart network by authenticated and
authorized users only.
Synchronizing the data between the physical and the digital twin
networks may increase the risk of sensitive data and information
leakage. Strict control and security mechanisms must be provided and
enabled to prevent data leaks.
11. Acknowledgements
Many thanks to the NMRG participants for their comments and reviews.
Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia, Jerome
Francois, Jordi Paillisse, Luis Miguel Contreras Murillo, Alexander
Clemm, Qiao Xiang, Ramin Sadre, Pedro Martinez-Julia, Wei Wang,
Zongpeng Du, and Peng Liu.
Diego Lopez and Antonio Pastor were partly supported by the European
Commission under Horizon 2020 grant agreement no. 833685 (SPIDER),
and grant agreement no. 871808 (INSPIRE-5Gplus).
12. IANA Considerations
This document has no requests to IANA.
13. Open issues
* The draft focuses on concept and architecture of digital twin
network, not including enabling technologies. Actually, each
'enabling technology' is worth of a separate draft to study in
details in future. A decision is needed that whether to add a
section to describe the enabling technologies in brief.
* Related to above issue, if section of enabling technologies is
added, recent technologies (e.g. Network connectivity, Real-time
data communication, Collaboration management, conflict detection
and resolution, etc.) recently discussed in the IRTF/IETF should
be described.
* In section of 'Sample Application Scenarios', to dig deeper into
one or two use cases.
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* On the research side, the idea behind digital twin networks is
reminiscent of earlier work from the 1990s that should be
referenced/acknowledged. Examples include the Shadow MIB concept,
Inductive Modeling Technique, etc.
14. 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.
[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.
[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.
[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.irtf-nmrg-ibn-concepts-definitions]
Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", Work in Progress, Internet-Draft, draft-
irtf-nmrg-ibn-concepts-definitions-06, 15 December 2021,
<https://www.ietf.org/archive/id/draft-irtf-nmrg-ibn-
concepts-definitions-06.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.
[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/".
[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.
[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://sites.google.com/view/tnt-2022/", 2022.
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Appendix A. Change Logs
v06 - v07: Addressed reviewer's comments from adoption call,
including below major changes.
* Resequenced the sections via adding more subsections on concepts
of digital twin network, removing the 'Requirements Language'
section, and moving ahead the 'Challenges' section.
* Cited more papers, or industrial information on digital twin
concepts and digital twin for networks.
* Added more information on describing the challenges and key
characteristics digital twin network.
* Removed previous open issue on investigating related digital twin
network work and identify the differences and commonalities, and
added several new open issues for future studys.
* Other Editorial changes.
v05 - v06: Addressed comments form meeting and maillist, to request
adoptoin call.
* Remove acronym DTN to avoid conflict with 'Delay Tolerant
Network';
* Elaborate the descriptoin of Digital Twin Network architecture
that supports multiple instances;
* Other Editorial changes.
04 - v05
* Clarify the difference between digital twin network platform and
traditional network management system;
* Add more references of researches on applying digital twin to
network field;
* Clarify the benefit of 'Privacy and Regulatory Compliance';
* Refine the description of reference architecture;
* Other Editorial changes.
v03 - v04
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* Update data definition and models definitions to clarify their
difference.
* Remove the orchestration element and consolidated into control
functionality building block in the digital twin network.
* Clarify the mapping relation (one to one, and one to many) in the
mapping definition.
* Add explanation text for continuous verification.
v02 - v03
* Split interaction with IBN part as a separate section.
* Fill security section;
* Clarify the motivation in the introduction section;
* Use new boilerplate for requirements language section;
* Key elements definition update.
* Other editorial changes.
* Add open issues section.
* Add section on application scenarios.
Authors' Addresses
Cheng Zhou
China Mobile
Beijing
100053
China
Email: zhouchengyjy@chinamobile.com
Hongwei Yang
China Mobile
Beijing
100053
China
Email: yanghongwei@chinamobile.com
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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
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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