Digital Twin Network:
Concepts and Reference ArchitectureChina MobileBeijing100053Chinazhouchengyjy@chinamobile.comChina MobileBeijing100053Chinayanghongwei@chinamobile.comChina MobileBeijing100053Chinaduanxiaodong@chinamobile.comTelefonica I+DSevilleSpaindiego.r.lopez@telefonica.comTelefonica I+DMadridSpainantonio.pastorperales@telefonica.comHuawei101 Software Avenue, Yuhua DistrictNanjingJiangsu210012Chinabill.wu@huawei.comOrangeRennes 35000Francemohamed.boucadair@orange.comOrangeRennes 35000Francechristian.jacquenet@orange.com
Networking
Internet Research Task ForceDigtial Twin; Digital Twin Network; IBN; Network
ManagementDigital 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.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 .A digital twin for networks platform can be built by applying Digital
Twin technologies to networks and creating a virtual image of real
network facilities (called herein, emulation). Basically, the digital
twin for networks is an expansion platform of network 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 real 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 real network, tweak
the network for better optimized behavior, run 'what-if' scenarios that
cannot be tested and evaluated easily in the real network. In addition,
service impact analysis tasks can also be facilitated.Intent-Based NetworkingArtificial IntelligenceContinuous Integration/Continuous
DeliveryMachine LearningOperations, Administration, and MaintenanceProduct Lifecycle ManagementThis document makes use of the following terms: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.a digital twin that is used
in the context of networking. This is also called, digital twin
for networks. See more in .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" . 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 . By providing a living copy of physical system,
digital twins bring numerous advantages, such as accelerated business
processes, enhanced productivity, and faster innovation with reduced
costs. So far, digital twin has been successfully applied in the
fields of intelligent manufacturing, smart city, or complex system
operation and maintenance to help with not only object design and
testing, but also management aspects .Compared with 'digital model' and 'digital shadow', the key
difference of 'digital twin' is the direction of data between the
physical and virtual systems . 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.Communication networks provide a solid foundation for implementing
various 'digital twin' applications. At the same time, in the face of
increasing business types, scale and complexity, a network itself also
needs to use digital twin technology to seek enhanced and optimized
solutions compared to relying solely on the real network. The
motivation for 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 scheme
to MEC network. [Dai2020] established a digital twin edge network for
mobile edge computing system, in which a twin edge server is used to
evaluate the state of entity server, and the twin mobile edge
computing system provides data for training offloading strategy.
[Nguyen2021] discusses how to deploy a digital twin for complex 5G
networks. [Hong2021] presents a digital twin platform towards
automatic and intelligent management for data center networks, and
then proposes a simplified the workflows of network service
management. [Dai2022] gives the concept of digital twin and proposes
an digital twin-enabled vehicular edge computing (VEC) network, where
digital twin can enable adaptive network management via the two-closed
loops between physical VEC networks and digital twins. In addition,
international workshops dedicated to digital twin in networking field
have already appeared, such as IEEE DTPI 2021&2022- Digital Twin
Network Online Session [DTPI2021, DTPI2022], and IEEE NOMS 2022 - TNT
workshop [TNT2022].Although the application of digital twin technology in networking
has started, the research of digital twin for networks technology is
still in its infancy. Current applications focus on specific scenarios
(such as network optimization), where network digital twin is just
used as a network simulation tool to solve the problem of network
operation and maintenance. Combined with the characteristics of
digital twin technology and its application in other industries, this
document believes that 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 real network in
the future, serving the application of network innovative technologies
such as network planning, construction, maintenance and optimization,
improving the automation and intelligence level of the 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 real network. Such virtual
representation of the network is meant to be used to analyze,
diagnose, emulate, and then control the real network based on data,
models, and interfaces. To that aim, a real-time and interactive
mapping is required between the real 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 .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. real network)
that are required by the models to represent and understand the
states and behaviors of the real-world twin. The data is characterized as the single source of
"truth" and populated in the data repository, which provides
timely and accurate data service support for building various
models.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 real 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 real network assets and, then,
instantiated to serve various network applications.Standardized interfaces can ensure the
interoperability of digital twin network. There are two major
types of interfaces: The interface between the digital twin network platform and
the real network infrastructure.The interface between digital twin network platform and
applications.The former provides real-time data collection and control
on the real network. The latter helps in delivering application
requests to the digital twin network platform and exposing the
various platform capabilities to applications.Used to identify the digital twin and the
underlying entities and establish a real-time interactive relation
between the real network and the twin network or between two twin
networks. The mapping can be: One to one (pairing, vertical): Synchronize between a real
network and its virtual twin network with continuous
flows.One to many (coupling, horizontal): Synchronize among
virtual twin networks with occasional data exchange.Such mappings provide a good visibility of actual status,
making the digital twin suitable to analyze and understand what is
going on in the real network. It also allows using the digital
twin to optimize the performance and maintenance of the real
network.The digital twin network constructed based on the four core
technology elements can analyze, diagnose, emulate, and control the
real network in its whole life cycle with the help of optimization
algorithms, management methods, and expert knowledge. One of the
objectives of such control is to master the digital twin network
environment and its elements to derive the required system behavior,
e.g., provide:repeatability: that is the capacity to replicate network
conditions on-demand.reproducibility: i.e., the ability to replay successions of
events, possibly under controlled variations.Note: Real-time interaction is not always mandatory for all twins.
When testing some configuration changes or trying some innovative
techniques, the digital twins can behave as a simulation platform
without the need of real time telemetry data. And even in this
scenario, it is better to have interactive mapping capability so that
the validated changes can be tested in real network whenever required
by the testers. In most other cases (e.g., network optimization,
network fault recovery), real-time interaction between virtual and
real network is mandatory. This way, digital twin network can help
achieve the goal of autonomous network or self-driven network.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 real network.The main difference between digital twin network and simulation
platform is the use of interactive virtual-real mapping to build
closed-loop network automation. Simulation platforms are the predecessor
of the digital twin network, one example of such a simulation platform
is network simulator [NS-3], which can be seen as a variant of digital
twin network but with low fidelity and lacking for interactive
interfaces to the real network. Compared with those classical
approaches, key benefits of digital twin network can be summarized as
follows: Using real-time data to establish high fidelity twins, the
effectiveness of network simulation is higher; then the simulation
cost will be relatively low.The impact and risk on running networks is low when automatically
applying configuration/policy changes after the full analysis and
required verifications (e.g., service impact analysis) within the
twin network.The faults of the real network can be automatically captured by
analyzing real-time data, then the correction strategy can be
distributed to the real network elements after conducting adequate
analysis within the twins to complete the closed-loop automatic
fault repair.The following subsections further elaborate such benefits in
details.Large scale networks are complex to operate. Since there is no
effective platform for simulation, network optimization designs have
to be tested on the real network at the cost of jeopardizing its daily
operation and possibly degrading the quality of the services supported
by the network. Such assessment greatly increases network operator's
Operational Expenditure (OPEX) budgets too.With a digital twin network platform, network operators can safely
emulate candidate optimization solutions before deploying them on the
real network. In addition, operator's OPEX on the real network
deployment will be greatly decreased accordingly at the cost of the
complexity of the assessment and the resources involved.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.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 real
network. In addition, it helps researchers to explore network
innovation (e.g., new network protocols, network AI/ML applications)
efficiently, and network operators to deploy new technologies quickly
with lower risks. Take AI/ ML application as example, it is a conflict
between the continuous high reliability requirement (i.e., 99.999%)
and the slow learning speed or phase-in learning steps of AI/ML
algorithms. With 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.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.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.According to [Hu2021], the main challenges in building and
maintaining digital twins can be summarized as the following five
aspects: Data acquisition and processingHigh-fidelity modelingReal-time, two-way communication between the virtual and the real
twinsUnified development platform and toolsEnvironmental coupling technologiesCompared 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: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.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.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.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 real network and digital twin network. Changes to
the digital object automatically drive changes in the real object
can be even challenging. To address these requirements, the function
and process of the data model need to be based on automated
processing mechanism under various network application scenarios. On
the one hand, it is needed to design a simplified process to reduce
the time cost for tasks in network twin as much as possible; on the
other hand, it is recommended to define the real-time requirements
of different applications, and then match the corresponding
computing resources and suitable solutions as needed to complete the
task processing in the twin.A digital twin network has to
synchronize all or subset of the data related to involved real
networks in real time, which inevitably augments the attack surface,
with a higher risk of information leakage, in particular. On one
hand, it is mandatory to design more secure data mechanism
leveraging legacy data protection methods, as well as innovative
technologies such as block chain. On the other hand, the system
design can limit the data (especially raw data) requirement on
building digital twin network, leveraging innovative modeling
technologies such as federal learning.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 (). 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.Based on the definition of the key digital twin network technology
elements introduced in , a digital twin network
architecture is depicted in . This
digital twin network architecture is broken down into three layers:
Application Layer, Digital Twin Layer, and Real Network Layer. All or subset of network elements in the
real network exchange network data and control messages with a
network digital twin instance, through twin-real control interfaces.
The real network can be a mobile access network, a transport
network, a mobile core, a backbone, etc. The real network can also
be a data center network, a campus enterprise network, an industrial
Internet of Things, etc. The real network
can span across a single network administrative domain or multiple
network administrative domains. And, the real network can include
both physical entities and some virtual entities (e.g. vSwitches,
NFVs, etc.), which together carry traffic and provide actual network
services. This document focuses on the IETF
related real network such as IP bearer network and data center
network.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
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 real network.Functional models refer to various data models used for
network analysis, emulation, diagnosis, prediction,
assurance, etc. The functional models can be constructed and
expanded by multiple dimensions: by network type, there can
be models serving for a single or multiple network domains;
by function type, it can be divided into state monitoring,
traffic analysis, security exercise, fault diagnosis,
quality assurance and other models; by network lifecycle
management, it can be divided into planning, construction,
maintenance, optimization and operation. Functional models
can also be divided into general models and special-purpose
models. Specifically, multiple dimensions can be combined to
create a data model for more specific application scenarios.
New applications might need new
functional models that do not exist yet. If a new model is
needed, ‘Service Mapping Models’ subsystem will
be triggered to help creating new models based on data
retrieved from ‘Data Repository’.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 real 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.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).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.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 , IPFIX , telemetry ) can be used to collect different type of network
data. YANG data models and associated mechanisms defined in enable subscriber-specific
subscriptions to a publisher's event streams. Such mechanisms can be
used by subscriber applications to request for a continuous and
customized stream of updates from a YANG datastore. Moreover, some
innovative methods (e.g., sketch-based measurement) can be used to
acquire more complex network data, such as network performance data.
Furthermore, data transformation and aggregation capabilities can be
used to improve the applicability on network modelling. Toward
building data repository for a digital twin system, data collection
tools and methods should be as lightweight as possible, so as to
reduce the volume of required network equipment resources, and
meaningful so it can be useful. Several solutions related to data
collection are work-in-progress in IETF/IRTF, e.g., adaptive
subscription ,
efficient data collection , and contextual
information .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.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 for more info).
A bottleneck structure is a computational graph that efficiently
captures the topology, the routing and flow properties of the network.
The graph embeds the latent relationships that exist between
bottlenecks and the application flows in a distributed system,
providing an efficient mathematical framework to compute the ripple
effects of perturbations (e.g., a flow arriving or departing from the
system, or the dynamic change in capacity of a wireless link, among
others). Because these perturbations can be seen as mathematical
derivatives of the communication system, bottleneck structures can be
used to compute optimized network configurations, providing a natural
engineering sandbox for building network models. One of the key
advantages of bottleneck structures is that they can be used to
compute (symbolically or numerically) key performance indicators of
the network (e.g., expected flow throughput, projected flow completion
time, etc.) without the need to use computationally intensive
simulators. This capability can be especially useful when building a
digital twin or a large-scale network, potentially saving orders or
magnitude in computational resources in comparison to simulation or
emulation-based approaches.The functional model aims to realize the dynamic evolution of
network performance evaluation and intelligent decision-making. Data
driven AI/ML algorithm will play a great role in building complex
network functional models. As a research hotspot in recent years, many
successfully cases have been demonstrated, such as [RouteNet],
[MimicNet], etc. In the future, in addition to improving the
generalization ability and interpretability of AI models, we also need
to focus on how to improve the real-time and interactivity of model
reasoning based on data and control in network digital twin layer.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 real network entity and the
network twin. Network Visibility technology can help users understand
the internal structure of the network, and also help mine valuable
information hidden in the network.Network Visibility can use algorithms such as hierarchical layout,
heuristic layout or force oriented layout (or a combination of several
algorithms) for topology layout. And the related topology data
can be acquired using solutions provided in ,
, , etc. Meanwhile,
digital twin network system can select different interaction methods
or combinations of interaction methods to realize the visual dynamic
interaction mapping of virtual and real networks. The data query
technology, such as SPARQL, can be used to express queries across
diverse data sources, whether the data is stored natively as RDF or
viewed as RDF via middleware.Based on the reference architecture, there are three types of
interfaces on building a digital twin network system.Network-facing interfaces are twin interfaces between the real
network and its twin entity. They are responsible for information
exchange between real network and network digital twin. The
candidate interfaces can be SNMP, NETCONF, etc.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.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 ), and
HTTP/3.0 (defined in ).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.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. 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 real network. Therefore, the digital
twin network can be an important enabler platform to implement IBN
systems and fooster their deployment.Digital twin network can be applied to solve different problems in
network management and operation.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.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.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.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.With the development of enterprise digitization, the number of
enterprise Internet of Objects (IoT) devices, virtualized Cloud
software inventory component (e.g., virtual firewall), and network
hardware inventory (e.g., switches, 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.Digital twin network management can be used as a means to ensure
consistent representation and reporting of inventory component types.
In doing so, the enforcement of security policies and assessment will
be further simplified. Such an approach will ease implementing a
unified control strategy for all inventory components types connected
to an enterprise network. It also make actors on assets more
accountable for breaching their compliance promises. Special care
should be considered to protect the inventory data since it may be
gather privacy-sensitive information.The network inventory management for twins or various inventory
components can be used, for example, to exercise the implication of
End of Life (EoL), dependency, and hardware dependency "what-if"
scenarios.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.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 real network and
digital counterpart network by authenticated and authorized users
only.Synchronizing the data between the real network and the twin network
may increase the risk of sensitive data and information leakage. Strict
control and security mechanisms must be provided and enabled to prevent
data leaks.Many thanks to the NMRG participants for their comments and reviews.
Thanks to Daniel King, Quifang Ma, Laurent Ciavaglia,
Jérôme François, Jordi Paillissé, Luis Miguel
Contreras Murillo, Alexander Clemm, Qiao Xiang, Ramin Sadre, Pedro
Martinez-Julia, Wei Wang, Zongpeng Du, 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).This document has no requests to IANA.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
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