Network Management C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: 6 January 2025 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
Q. Wu
Huawei
M. Boucadair
C. Jacquenet
Orange
5 July 2024
Network Digital Twin: Concepts and Reference Architecture
draft-irtf-nmrg-network-digital-twin-arch-06
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, 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 such technology's
benefits and key challenges.
Discussion Venues
This note is to be removed before publishing as an RFC.
Discussion of this document takes place on the Network Management
Research Group mailing list (nmrg@irtf.org), which is archived at
https://mailarchive.ietf.org/arch/browse/nmrg.
Source for this draft and an issue tracker can be found at
https://github.com/cheneyzhoucheng/network-digital-twin.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 6 January 2025.
Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document.
Table of Contents
1. Introduction
2. Terminology
2.1. Acronyms and Abbreviations
2.2. Definitions
3. Introduction of Concepts
3.1. Background of Digital Twin
3.2. Digital Twin for Networks
4. Characteristics of Network Digital Twin
5. Benefits of Network Digital Twin
5.1. Optimized Network Total Cost of Operation
5.2. Optimized Decision Making
5.3. Safer Assessment of Innovative Network Capabilities
5.4. Privacy and Regulatory Compliance
5.5. Customized Network Operation Training
6. Challenges to Build a Network Digital Twin
7. NDT Functional Components
8. A Sample NDT Realization
9. Enabling Technologies to Build Network Digital Twin
9.1. Data Collection and Data Services
9.2. Network Modeling
9.3. Network Visualization
9.4. Interfaces
9.5. Twinning Management
10. Interaction with Intent-Based Networking (IBN)
11. Sample Application Scenarios
11.1. Human Training
11.2. Machine Learning Training
11.3. DevOps-Oriented Certification
11.4. Network Fuzzing
11.5. Network Inventory Management
12. Research Perspectives: A Summary
13. Security Considerations
14. IANA Considerations
15. Open issues
16. Informative References
Acknowledgments
Contributors
Authors' Addresses
1. Introduction
The rapid expansion of network scale and the increasing demands on
these networks necessitate their dynamic adaptation to customer
needs, presenting significant challenges for network operators.
Network operation and maintenance are becoming increasingly complex
due to the advanced nature of the networks and the sophisticated
services they provide. Consequently, introducing innovations in
network technologies, management, and operations is becoming more
challenging due to the high risk of disrupting existing services and
the elevated costs of trials without reliable emulation platforms.
A Digital Twin is a real-time digital representation of a physical
entity. It features virtual-reality interrelation and real-time
interaction, iterative operation and process optimization, and full
life-cycle, comprehensive data-driven network infrastructure.
Digital twins have gained widespread recognition in academic
publications and are now being widely adopted for Industry 4.0 use
cases. The reader may refer to Section 3 for more details,.
A digital twin for networks can be created by applying Digital Twin
technologies to networks, resulting in a virtual replica of real
network facilities (emulation). A Network Digital Twin (NDT) is an
advanced platform for network emulation, serving as a tool for
scenario planning, impact analysis, and change management. Unlike
conventional network simulation, it features an interactive virtual-
real mapping and a data-driven approach to establish closed-loop
network automation.
Integrating a Network Digital Twin into network management allows
engineers to assess, model, and refine optimization strategies under
real conditions but in a risk-free environment. This ensures that
only the most effective changes are implemented in the real network,
following thorough validation and control checks. Moreover, a
network digital twin captures and aggregates critical data for
analyzing the root causes of network failures, anomalies,
vulnerabilities, etc. It also offers a sandbox for testing
hypotheses, exercising mitigation scenarios, and validating data-
driven insights without affecting end-users.
Through the real-time data interaction between the real network and
its twin network(s), the Network Digital Twin (NDT) platform will
provide the data for NDT-based applications and network designers to
achieve greater simplification, automation, resilience testing
("what-if scenarios"), and full life-cycle operation and
infrastructure maintenance.
2. Terminology
2.1. Acronyms and Abbreviations
IBN: Intent-Based Networking
AI: Artificial Intelligence
CI/CD: Continuous Integration/Continuous Delivery
ML: Machine Learning
OAM: Operations, Administration, and Maintenance
2.2. Definitions
This document makes use of the following terms:
Digital Twin: Digital counterpart of a physical system (twin) that
captures its attributes, behavior, and interactions and is
(continually) updated with the latter's performance, maintenance,
and health status data throughout the physical system's life
cycle.
Network Digital Twin: A digital representation that is used in the
context of Networking and whose physical counterpart is a data
network (e.g., provider network or enterprise network). This is
also called, digital twin for networks. See more in Section 4.
Physical Network: Object, system, process, software, or environment
that the digital twin is designed to replicate and represent
virtually.
3. Introduction of Concepts
3.1. Background of Digital Twin
The concept of the "twin" dates to the National Aeronautics and Space
Administration (NASA) Apollo program in the 1970s, where a replica of
space vehicles on Earth was built to mirror the condition of the
equipment during the mission [Rosen2015].
In 2003, Digital Twin was attributed to John Vickers by Michael
Grieves in his Product Lifecycle Management (PLM) course as "virtual
digital representation equivalent to physical products"
[Grieves2014]. Digital twin can be defined as a virtual instance of
a physical system (twin) that is continually updated with the
latter's performance, maintenance, and health status data throughout
the physical system's life cycle [Madni2019]. By providing a living
copy of physical system, digital twins bring numerous advantages,
such as accelerated business processes, enhanced productivity, and
faster innovation with reduced costs. So far, digital twin has been
successfully applied in the fields of intelligent manufacturing,
smart city, or complex system operation and maintenance to help with
not only object design and testing, but also management aspects
[Tao2019].
Compared with 'digital model' and 'digital shadow', the key
difference of 'digital twin' is the direction of data between the
physical and virtual systems [Fuller2020]. Typically, when using a
digital twin, the (twin) system is generated. Then a partial or full
synchronization of data flows in both directions between physical and
digital components, so that control data can be sent, and changes
between systems' physical and digital objectives are automatically
represented. This behavior is unlike a 'digital model' or 'digital
shadow', which are usually synchronized manually, lacking control
data, and might not have integrated a full cycle of data.
At present (2024), 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 real network.
The motivation for Network Digital Twin 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 Network Digital Twin can be regarded as
an indispensable part of the overall network system and provides a
general architecture involving the whole life cycle of a "real"
(typically, 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.
4. Characteristics of Network Digital Twin
So far, there is no standard definition for characteristic of
"network digital twin" within the networking industry. This document
introduces five key elements (i.e., data, models, mapping,
interfaces, and logic) to characterize the Network Digital Twin.
These five elements can be integrated into a network management
system to analyze, diagnose, emulate, and control the real network.
To that aim, a real-time and interactive mapping is required between
the real network and its virtual twin network. Whether a Digital
Twin supports all or a subset of the functions above (i.e., analyze,
diagnose, emulate, and control) is deployment-specific.
Referring to the characteristics of digital twins in other industries
and the characteristics of networking itself, the digital twin
network should involve at least five key elements: data, mapping,
models, interfaces, and logic, as shown in Figure 1. This provides
the information to the application or entity that consumes the
information to analyze, diagnose, or control.
+-------------------------------------------------+
| Logic: |
| Analyze, Diagnose, Optimize, Control, Emulate |
| |
+-------------------------------------------------+
| | | |
| +-------------+ +--------------+ |
| | | | | |
| | Mapping |------------| Interface | |
| | | | | |
| +-------------+ +--------------+ |
| | | |
| | | |
| | +----------------------+ | |
| | | Network Digital Twin | | |
| | +----------------------+ | |
| | | |
+------------+ +-----------+
| | | |
| Models | | Data |
| |------------------------| |
+------------+ +-----------+
Figure 1: Key Elements of Network Digital Twin
Data: A Network Digital Twin 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.
Models: Models provide a basis for emulating changes in the
configuration, state or use of network elements and resources,
providing information on how the real network operates and
generating reasoning data that may be utilized in operational
decision-making.
Various types of models including service models, data models,
dataset models, transfer matrices, knowledge graphs etc.can be
used to represent the real network assets and their behaviours,
and composed to emulate network changes and behaviours, serving
the analysis needs of various use case-based network applications.
Interfaces: Standardized interfaces ensure the interoperability of
network digital twin with real network operations systems. There
are two major types of interfaces:
* The interface between the Network Digital Twin platform and the
real network infrastructure, directly or through an associated
operations (i.e. planning, control, management) system.
* The interface between Network Digital Twin platform and
operations applications that consume the information provided
by the NDT.
The former provides real-time data collection from the real
network. The latter helps in delivering application requests to
the Network Digital Twin 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 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 Network Digital Twin, constructed based on the four core
technology elements, can provide crucial emulation-driven information
to support analysis, diagnosis, and control of the real network,
through its whole life cycle, with the help of optimization
algorithms, management methods, and expert knowledge.
The Network Digital Twin environment and its elements must be
controlled and driven to support required behaviors in use, e.g., to
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.
and "the mirroring pace and scope" should be controlled for a given
twin usage.
Note: Real-time interaction is not always mandatory for all NDT
use cases. For example, when assessing some configuration changes
or emulating some innovative techniques, the digital twin can
behave as an isolated simulation platform without the need of
real-time telemetry data. It might be useful to have interactive
mapping capability so that the validated changes can be evaluated
under real network conditions whenever required by the testers.
Whether real-time interaction between virtual and real network is
mandatory is a configurable parameter. Adequate validation guards
have to be enforced at both twin and physical network. Enabling
real-time interaction in Network Digital Twin is a catalyst to
achieving autonomous networks or self-driven network.
Logic: Network digital twin facilitates optimal resource allocation
and configuration, enhancing efficiency and performance. They
enable comprehensive troubleshooting maintenance and control by
diagnosing issues in the digital twin. Moreover, Network Digital
Twins play a crucial role in planning and deployment, allowing for
the simulation of new designs and configurations to anticipate
their effects before implementation.
5. Benefits of Network Digital Twin
Network digital twin 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 Network Digital Twin and simulation
platforms is the use of interactive virtual-real mapping to support
integration of model (e.g., emulation) based analysis in real network
operations environments, up to and including closed loops for network
operations automation. Simulation platforms can be considered as a
predecessor of the Network Digital Twin, one example of such a
simulation platform is network simulator [NS-3], which can be seen as
a variant of Network Digital Twin but with low fidelity and lacking
for interactive interfaces to the real network. Compared with those
classical approaches, key benefits of Network Digital Twin can be
summarized as follows:
(a) Using real-time data to establish high fidelity twins, the
effectiveness of network simulation is higher; then the
simulation cost will be relatively low.
(b) 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.
(c) 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.
5.1. Optimized Network Total Cost of Operation
Large scale networks are complex to operate. Since there is no
effective platform for simulation, network optimization designs have
to be tested on the 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 Network Digital Twin 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.
5.2. Optimized Decision Making
Traditional network operation and management mainly focus on
deploying and managing running services, but hardly support
predictive maintenance techniques.
Network digital twin can combine data acquisition, big data
processing, and AI-based modeling to assess the status of the
network, but also to predict future trends, and better organize
predictive maintenance. The ability to reproduce network behaviors
under various conditions facilitates the corresponding assessment of
the various evolution options as often as required.
5.3. Safer Assessment of Innovative Network Capabilities
Testing a new feature in an operational network is not only complex,
but also extremely risky. Service impact analysis is required to be
adequately achieved prior to effective activation of a new feature.
Network digital twin 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 Network Digital Twin, AI/ML can complete the
learning and training with the sufficient data before deploying the
model in the real network. This would encourage more network AI
innovations in future networks.
5.4. Privacy and Regulatory Compliance
The requirements on data confidentiality and privacy on network
providers increase the complexity of network management, as decisions
made by computation logics such as an SDN controller may rely upon
the packet payloads. As a result, the improvement of data-driven
management requires complementary techniques that can provide a
strict control based upon security mechanisms to guarantee data
privacy protection and regulatory compliance. This may range from
flow identification (using the archetypal five-tuple of addresses,
ports and protocol) to techniques requiring some degree of payload
inspection, all of them considered suitable to be associated to an
individual person, and hence requiring strong protection and/or data
anonymization mechanisms.
With strong modeling capability provided by the Network Digital Twin,
very limited real data (if at all) will be needed to achieve similar
or even higher level of data-driven intelligent analysis. This way,
a lower demand of sensitive data will permit to satisfy privacy
requirements and simplify the use of privacy-preserving techniques
for data-driven operation.
5.5. Customized Network Operation Training
Network architectures can be complex, and their operation requires
expert personnel. Network digital twin offers an opportunity to
train staff for customized networks and specific user needs. Two
salient examples are the application of new network architectures and
protocols or the use of "cyber-ranges" to train security experts in
threat detection and mitigation.
6. Challenges to Build a Network Digital Twin
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, communication between the virtual and the real twins
* Unified development platform and tools
* Environmental coupling technologies
Compared with other industrial fields, digital twin in networking
field has its unique characteristics. On the 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 types of network elements and topologies in the network
field; and the network size is characterized by the number 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 and the design and implementation of relevant models.
The requirements of the software and hardware of the Network
Digital Twin system will be even more constrained. 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 network digital twin, 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 scale networks. 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, and the processing of model simulation and
verification through a digital network twin will introduce 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, keeping Network Digital Twins in sync or auto-
sync between real network and Network Digital Twin is challenging.
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.
Security risks: A Network Digital Twin 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 and innovative technologies such as block
chain. On the other hand, the system design can limit the data
(especially raw data) requirement for building digital twin
network, leveraging innovative modeling technologies such as
federal learning.
To address the above listed challenges, it is important to agree on a
unified architecture of Network Digital Twin, which defines the main
functional components and interfaces (Section 7). 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.
7. NDT Functional Components
Based on the definition of the key Network Digital Twin elements
introduced in Section 4, a Network Digital Twin architecture is
depicted in Figure 2.
+---------------------------------------------------------+
| +-------+ +-------+ +-------+ |
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
|Capability Exposure| Intent Input
| |
+-------------+-------------------v-----------------------+
| Instance of Network Digital Twin |
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Repo- | | +-----+-----^------+ | | Twin | |
| | sitory | | | | | | Network| |
| | | | +-----v-----+------+ | | Mgmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^----------------------------+-------------------+
| |
| data collection | control
+--------+----------------------------v-------------------+
| Real Network |
| |
+---------------------------------------------------------+
Figure 2: Reference Architecture of Network Digital Twin
Section 4 describes functional characteristics or elements of NDT in
four principal classes: data, models, interfaces, and mappings.
This section describes the important functional components of NDTs -
reflecting these functional elements - in greater detail. It also
describes how an NDT consisting of these components may be used in
operations systems to deliver the various functional NDT use cases.
The core functional components of an NDT may be posited as follows: a
Data Repository component, a Service Mapping Models component, and an
NDT Management component. These key components might be placed
within one single network administrative domain and provide service
to the operations applications (e.g., SDN controllers, emulation
applications) within that domain or in other network administrative
domains. They may be also placed in each network administrative
domain and coordinate among each other to provide services to
operations applications. One or multiple NDT instances may be
maintained and operated in service of a given real network.
The Data Repository component is responsible for collecting and
storing network data. It collects and updates the real-time
operational and instrumentation data of the various network elements
through the appropriate real network-facing interface, as well as
from other operations system components. It also provides data
services (e.g., fast retrieval, concurrent conflict handling, batch
service) through appropriate interfaces to a Service Mapping Models
component.
Service Mapping Models complete data modeling, and provide data or
other functional model instances supporting various network
applications. Models include two major types, basic and functional
models:
o Basic models refer to network element models and network topology
models used to reflect the basic configuration, environment
information, operational state, link topology, etc. of the network
and its elements. o Functional models refer to various data or other
models used to generate information supporting network analysis,
emulation, diagnosis, prediction, assurance, etc. The functional
models can be constructed and expanded in various ways: by network
type; there can be models serving single or multiple network domains;
by function type. Functional models and the information they
generate can relate to state monitoring, traffic analysis, security
exercise, fault diagnosis, quality assurance and various network
lifecycle management goal - such as planning, construction,
maintenance, optimization and operation. Functional models can also
be divided into general models and special-purpose models. Multiple
models can be combined to create a model for more specific
application scenarios. New applications might need new functional
models that do not yet exist. If a new model is needed, the Service
Mapping Models subsystem may help to create new models based on data
retrieved from the Data Repository.
The Network Digital Twin Management component manages the NDT
operation and its subcomponents to useful effect, serving
applications that require and make use of the information generated
by the NDT. It manages the session-based operation of the NDT,
managing the life-cycle of these operations under the direction of
associated applications; it monitors the performance and resource
consumption of the NDT (including individual models); and controls
various operational aspects of the NDT, including topology
management, model management and security management.
The "real network" – the physical counter-part of an NDT - 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. It can include
both physical entities and some virtual entities (e.g., vSwitches),
which together carry traffic and provide actual network services.
All or subset of network elements in the real network deliver network
data, directly or through other systems, to the NDT, through
appropriate interfaces. Network elements may receive control inputs,
through specific interfaces, from operations systems in which NDTs
play a role. This document focuses on the IETF related real network
such as IP bearer network and data center network.
8. A Sample NDT Realization
Considerable industry work and research has focused on automation-
supporting network systems. For example, [ETSI-GS-ZSM-002] describes
a framework architecture for network automation. It uses so-called
management services as a fundamental conceptual unit of currency, and
describes the enablement of automation use cases through composition
and extensions of such management services. For example, a closed-
loop might be represented as a composition of appropriate data,
analytics, intelligence/decision, and orchestration/control services.
The role and utility of NDT may be represented architecturally by
following similar principles, e.g., [ETSI-GS-ZSM-015] or [RFC8969].
As described in Section 7, an NDT instantiation encompasses models,
data, mapping, and interfaces. These components then work in
composition with other functions or services to deliver an overall
functional architecture matching specific NDT use cases.
For example: an NDT instance may be used as a core element of an
intent-drive network controller. In such a case, an "outer" closed-
loop (or, intent-assurance closed-loop) would detect gaps between
target service objectives set by intents and actual observed service
characteristics, find and proposed candidate mitigation solutions to
soften the observed deviation, and drive the enforcement of the
mitigation in the network. Finding such mitigations would rely on
multiple "inner loops" that include an NDT: for example, prospective
solutions would be proposed, their impacts on services evaluated by
the NDT acting as a "sandbox" in virtual space, and the process might
be iterated until a satisfactory solution is found. At that point,
the selected mitigation is passed to the outer loop for actuation.
Many automation use cases may be thought of as following a similar
pattern: a solution corresponding to some kind of optimization
criteria is found through iteration in virtual space using an NDT
instance; the solution is then placed at the disposal of other,
active components of the operations system. However, all use cases
involving NDTs can be represented as some composition of the core
data/modeling functions, and appropriate other functions/services.
+--------------------------------------------------------------+
| |
| Service Demand Generators |
| |
+-----------+-------------------------------------------^------+
| |
Service Intents Intents Report
| |
V |
+-------------------------------------------------------+------+
| |
| +-------------+ |
| | | Network Controller |
| | Intent | |
| | Translation | |
| | | |
| +---+---------+ |
| | |
| | Outer Loop |
| | +----------------------------------------------+ |
| | | | |
| | | +---------------------------------------+ | |
| | | | Inner Loop | | |
| | | | +------------------------------------|| | |
| | | | | +----------------------------+ || | |
| | | | | | NDT | || | |
| V V | | |+------+ +-------+ +------+| || | |
|+------------+-+ | +-> || Data | | Models| | NDT |+---+| | |
|| | | || | | | | Mgmt || | | |
|| Service Data | | |+------+ +-------+ +------+| | | |
|| vs. Intents| | | | | | |
|| | | +----------------------------+ | | |
|+--------------+ +---------------------------------------+ | |
| | +----------------+ | |
| | | | | |
| | | Orchestration | | |
| +-----> | Control +-----+ |
| +----------------+ |
| |
+-----------^-------------------------------------+------------+
| |
|Data Collection Control|
| |
+-----------+-------------------------------------V------------+
| |
| Physical Netework |
| |
+--------------------------------------------------------------+
Figure 3: Example of Detailed NDT Architecure
9. Enabling Technologies to Build Network Digital Twin
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.
9.1. Data Collection and Data Services
Data collection technology is the foundation of building data
repository for Network Digital Twin. Target driven mode should be
adopted for data collection from heterogeneous data sources. The
type, frequency and data collection method shall meet the
requirements of the Network Digital Twin application. 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], and 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
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 and 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.
9.2. 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.
Mathematical abstraction methods can be used for large-scale networks
to build basic network models efficiently. Knowledge graph, network
calculus, and formal verification can be candidate methods. Some
relevant research has emerged in recent years, such as [Hong2021],
[G2-SIGCOMM], and [DNA-2022]. Moving forward, improving the
extensibility and accuracy of the models represents a significant
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
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 the capacity of a wireless
link, among others). Because these perturbations are 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) without using 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 algorithms will play a great role in building complex network
functional models. As a research hotspot in recent years, many
successful cases have been demonstrated, such as [RouteNet],
[MimicNet], etc. In the future, in addition to improving the
generalization ability and interpretability of AI models, there is
also a 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.
9.3. Network Visualization
It is the internal requirement of the Network Digital Twin 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 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. The related topology data
can be acquired using solutions provided in [RFC8345], [RFC8346],
[RFC8944], etc. Meanwhile, Network Digital Twin 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 express
queries across diverse data sources, whether the data is stored
natively as RDF or viewed as RDF via middleware.
9.4. Interfaces
Based on the reference architecture, there are three types of
interfaces on building a Network Digital Twin system:
(d) 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.
(e) 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.
(f) 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 [RFC7622] or HTTP/3.0
[RFC9114].
All these interfaces are recommended to be open and standardized so
as to avoid either hardware or software vendor lock and achieve
interoperability. Besides the interfaces listed above, some new
interfaces or protocols can be created to better serve digital twin
network system.
9.5. Twinning Management
Twinning management is the key to the efficient deployment and
potential value of Network Digital Twin systems in production
networks. Twinning management technology inputs all information and
data from each step of the network business into the constructed
model by constructing digital threads for optimization, prediction,
and guidance. Then, the implementation results are analyzed to see
if they meet expectations, and any actions are fed back to form a
closed loop. Twinning management involves various network components
(e.g., controller, orchestrator) and domains (security, for example)
from end to end, including, but not limited to, the following main
technologies:
* Orchestration of twins: Manage and organize multiple twin model
instances, including the creation, deletion, storage, version
control, and deployment of model instances, and arrange required
modeling resources as needed to maximize resource utilization
efficiency.
* Collaboration Management: Coordinate multiple participants, such
as network administrators, data scientists, security teams, etc.,
to ensure the accuracy and real-time performance of the twins.
Involve collaborative tools, workflow design, data sharing, and
permission control to promote cooperation and information sharing
among all parties.
* Conflict Detection and Resolution: Identify and address conflicts,
including user intents, access control policies, or multiple
applications interacting within the digtial twin network system.
Conflict detection and resolution techniques may use various
mechanisms, such as rule-based policies, role-based access
control, or dynamic conflict resolution algorithms (e.g.,
[Pradeep2022] and [Zheng2022]).
* Energy-Efficient Twinning: Focus on energy efficiency in digital
twin network system. It includes monitoring and optimizing the
energy consumption of both network equipment and digital twin
system operation, reducing the energy expenditure of network
operation, and achieving the goal of a green (energy efficient)
network.
10. Interaction with Intent-Based Networking (IBN)
Intent-based, 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 adjust policies and validate real-time situations.
IBN can be envisaged in a Network Digital Twin context to show how
Network Digital Twin improves the efficiency of deploying network
innovation. Several rounds of adjustment and validation can be
emulated on the digital twin platform instead of directly impacting
real network during the testing phase. Therefore, the digital twin
network can be an important enabler platform for implementing IBN
systems and fostering their deployment.
11. Sample Application Scenarios
Network digital twin can be applied to solve different problems in
network management and operation.
11.1. Human Training
The usual approach to network OAM with procedures applied by humans
is open to errors in all these procedures, whigh impact 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 multiple actions. These processes are not
intuitive and require training to learn how to respond.
The use of Network Digital Twin 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. Network digital twin will offer realistic
environments, fitted to the real production networks.
11.2. Machine Learning Training
Machine learning requires data and their context to be available in
order to be applied. 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.
Network digital twin 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 Network Digital Twin and not to the
production network, allowing information access by third parties,
without impacting data privacy.
11.3. DevOps-Oriented Certification
The potential application of CI/CD models network management
operations increases the risk associated to the deployment of non-
validated updates, which 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 Network Digital Twin environment replicating the
network particularities as a previous step to production release.
Network digital twin control functional block supports such dynamic
mechanisms required by DevOps procedures.
11.4. 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.
Network digital twin allows to apply fuzzing testing techniques on a
twin network environment, with interactions and conditions similar to
the production network, permitting the identification of
vulnerabilities, bugs and zero-day attacks before production
delivery.
11.5. Network Inventory Management
With the development of enterprise digitization, the number of
enterprise Internet of Objects (IoT) devices, virtualized Cloud
software inventory components (e.g., virtual firewall), and network
hardware inventory (e.g., switches or 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 tracked in different places without formal
synchronization procedures.
Network digital twin 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 assessments
will be further simplified. Such an approach will ease the
implementation of a unified control strategy for all inventory
component types connected to an enterprise network. It also makes
actors on assets more accountable for breaching their compliance
promises. Special care should be considered to protect the inventory
data since it may 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.
12. Research Perspectives: A Summary
Research on Network Digital Twin has just started. This document
presents an overview of the Network Digital Twin concepts and
reference architecture. As digital twin technology develops, further
investigation of Network Digital Twin scenarios, requirements,
architecture, and key enabling technologies should be investigated by
the industry to accelerate the implementation and deployment of
digital twin network.
13. Security Considerations
This document describes concepts and definitions of NDT. As this
document presents system architecture, the following security
considerations are abstract and generic, i.e., they provide mainly
principles, guidelines or requirements. However, the implementation
and deployment of NDT will need to carefully investigate the
following security considerations, which may be categorized into
different aspects:
* Data Management
Synchronization: Synchronizing the data between the real and twin
networks may increase the risk of sensitive data and information
leakage.
Data Access: Strict control and security mechanisms must be provided
and enabled to prevent data leaks. Also, appropriate access
rights must be provisioned to prevent unauthorized entities to
access sensitive data (e.g., logging data used for legal data
retention).
* Data Security and Privacy Protection.
Confidentiality: Ensuring that sensitive data used in the digital
twin is protected from unauthorized access. This includes
encrypting data both at rest and in transit.
Integrity: Ensuring that the data used in and produced by the
digital twin is accurate and unaltered. This can be achieved
through cryptographic hash functions and other integrity
verification methods.
Access Control: Implementing strict access control measures ensures
that only authorized users can access, modify, or interact with
the digital twin. This includes using multi-factor authentication
(MFA) and role-based access control (RBAC).
* System Security
Authentication and Authorization: Ensuring robust authentication and
authorization mechanisms to prevent unauthorized access to the
digital twin environment.
Vulnerability Management: Regularly auditing, updating, and patching
the digital twin software and underlying infrastructure to
mitigate vulnerabilities.
Monitoring and Logging: Implementing comprehensive logging and
monitoring to detect and respond to security incidents in real-
time.
* Network Security
Segmentation: Isolating the NDT environment from the main
operational network to limit the potential impact of a security
breach.
Encryption: Encrypting network communications to prevent
interception and eavesdropping.
Access Control List: Deploying , e.g., firewalls and IDS/IPS with
appropriate policies to protect the NDT from external and internal
threats.
* Operational Security
System Access: Data verification, model validation, and mapping
operations between the real and digital counterpart networks by
authenticated and authorized users only.
Secure Development Practices: Ensuring the NDT software is developed
following secure coding practices to minimize vulnerabilities.
Incident Response: Having a well-defined incident response plan
(e.g., playbooks) to address and mitigate any security incidents
quickly.
Regular Audits and Assessments: Conduct security audits and risk
assessments to identify and address potential security gaps.
* Resilience and Reliability
Redundancy: Implementing redundancy and failover mechanisms ensures
the digital twin remains operational during and after a security/
failure incident.
Backup and Recovery: Regularly back up NDT data and have a robust
recovery plan to restore operations in case of data loss or
corruption.
14. IANA Considerations
This document has no requests to IANA.
15. Open issues
Refer to: https://github.com/cheneyzhoucheng/network-digital-twin/
issues (https://github.com/cheneyzhoucheng/network-digital-twin/
issues).
16. Informative References
[Dai2020] IEEE Transactions on Industrial Informatics, "Deep
Reinforcement Learning for Stochastic Computation
Offloading in Digital Twin Networks", August 2020.
[Dai2022] Journal of Communications and Information Networks,
"Adaptive Digital Twin for Vehicular Edge Computing and
Networks", March 2022.
[DNA-2022] NSDI 22, "Differential Network Analysis, USENIX Symposium
on Networked Systems Design and Implementation", 2023.
[Dong2019] IEEE Transactions on Wireless Communications, "Deep
Learning for Hybrid 5G Services in Mobile Edge Computing
Systems: Learn from a Digital Twin", July 2019.
[DTPI2021] "IEEE International Conference on Digital Twins and
Parallel Intelligence - Digital Twin Network Session",
July 2021, <https://www.dtpi.org/video/10>.
[DTPI2022] "IEEE International Conference on Digital Twins and
Parallel Intelligence - Digital Twin Network Session",
October 2022, <https://c.trvqd.com/#/television?id=1584825
225713631235&preview=2>.
[ETSI-GS-ZSM-002]
"Zero-touch network and Service Management (ZSM);
Reference Architecture", August 2019,
<https://www.etsi.org/deliver/etsi_gs/
ZSM/001_099/002/01.01.01_60/gs_ZSM002v010101p.pdf>.
[ETSI-GS-ZSM-015]
"Zero-touch network and Service Management (ZSM); Network
Digital Twin", February 2024,
<https://www.etsi.org/deliver/etsi_gr/
ZSM/001_099/015/01.01.01_60/gr_ZSM015v010101p.pdf>.
[EVE-NG] "Emulated Virtual Environment Next Generation", n.d.,
<https://www.eve-ng.net/>.
[Fuller2020]
IEEE Access, "Digital Twin: Enabling Technologies,
Challenges and Open Research", 2020.
[G2-SIGCOMM]
ACM SIGCOMM, "Designing data center networks using
bottleneck structures", August 2021.
[G2-SIGMETRICS]
ACM SIGMETRICS, "On the Bottleneck Structure of
Congestion-Controlled Networks", December 2019.
[GNS-3] "Graphical Network Simulator-3, GNS3", n.d.,
<https://www.gns3.com/>.
[Grieves2014]
"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] ACM SIGCOMM 2021 Workshop on Network-Application
Integration (NAI' 21), "NetGraph: An Intelligent Operated
Digital Twin Platform for Data Center Networks", 2021.
[Hu2021] Journal of Intelligent Manufacturing and Special
Equipment, "Digital twin: a state-of-the-art review of its
enabling technologies, applications and challenges", 2021.
[I-D.claise-opsawg-collected-data-manifest]
Claise, B., Quilbeuf, J., Lopez, D., Martinez-Casanueva,
I. D., and T. Graf, "A Data Manifest for Contextualized
Telemetry Data", Work in Progress, Internet-Draft, draft-
claise-opsawg-collected-data-manifest-06, 10 March 2023,
<https://datatracker.ietf.org/doc/html/draft-claise-
opsawg-collected-data-manifest-06>.
[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://datatracker.ietf.org/doc/html/draft-
giraltyellamraju-alto-bsg-requirements-03>.
[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-05, 13 June 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-netconf-
adaptive-subscription-05>.
[I-D.zcz-nmrg-digitaltwin-data-collection]
Zhou, C., Chen, D., Martinez-Julia, P., and Q. Ma, "Data
Collection Requirements and Technologies for Digital Twin
Network", Work in Progress, Internet-Draft, draft-zcz-
nmrg-digitaltwin-data-collection-03, 9 July 2023,
<https://datatracker.ietf.org/doc/html/draft-zcz-nmrg-
digitaltwin-data-collection-03>.
[ISO-2021] ISO, "Digital Twin manufacturing framework - Part 2:
Reference architecture: ISO/CD 23247-2", 2021,
<https://www.iso.org/standard/78743.html>.
[Madni2019]
"Leveraging digital twin technology in model-based systems
engineering", January 2019.
[MimicNet] ACM SIGCOMM 2021 Conference (SIGCOMM ’21), "MimicNet: Fast
Performance Estimates for Data Center Networks with
Machine Learning", 2021.
[Mininet] "Mninet: An Instant Virtual Network on your Laptop", n.d.,
<http://mininet.org/>.
[Natis-Gartner2017]
"Innovation insight for digital twins - driving better
IoT-fueled decisions", 2017,
<https://www.gartner.com/en/documents/3645341>.
[Nguyen2021]
IEEE Communications Magazine, "Digital Twin for 5G and
Beyond", February 2021.
[NS-3] "Network Simulator, NS-3", n.d., <https://www.nsnam.org/>.
[Pradeep2022]
"Conflict Detection and Resolution in IoT Systems: A
Survey. IoT 2022", February 2022.
[RESTFul] O'Reilly Media, Inc, "RESTful Web APIs", 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/rfc/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/rfc/rfc7011>.
[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/rfc/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/rfc/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/rfc/rfc8346>.
[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/rfc/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/rfc/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/rfc/rfc8944>.
[RFC8969] Wu, Q., Ed., Boucadair, M., Ed., Lopez, D., Xie, C., and
L. Geng, "A Framework for Automating Service and Network
Management with YANG", RFC 8969, DOI 10.17487/RFC8969,
January 2021, <https://www.rfc-editor.org/rfc/rfc8969>.
[RFC9114] Bishop, M., Ed., "HTTP/3", RFC 9114, DOI 10.17487/RFC9114,
June 2022, <https://www.rfc-editor.org/rfc/rfc9114>.
[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/rfc/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/rfc/rfc9315>.
[Rosen2015]
IFAC-Papersonline, "About the importance of autonomy and
DTs for the future of manufacturing", 2015.
[RouteNet] IEEE Journal on Selected Areas in Communication (JSAC),
"RouteNet:Leveraging Graph Neural Networks for network
modeling and optimization in SDN", October 2020.
[Tao2019] IEEE Transactions on Industrial Informatics, "Digital Twin
in Industry: State-of-the-Art", April 2019.
[TNT2022] "IEEE International workshop on Technologies for Network
Twins", 2022, <https://noms2022.ieee-noms.org/ws4-1st-
international-workshop-technologies-network-twins-tnt-
2022>.
[Zheng2022]
"Intent Based Networking management with conflict
detection and policy resolution in an enterprise network,
Computer Networks, Volume 219", December 2022.
Acknowledgments
Many thanks to the NMRG participants for their comments and reviews.
Thanks to 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, Peng
Liu, and Albrecht Schwarz.
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).
Daniel King was partly supported by the UK Department for Science,
Innovation and Technology under the Future Open Networks Research
Challenge project TUDOR (Towards Ubiquitous 3D Open Resilient
Network).
Contributors
Christopher Janz
Huawei
Email: christopher.janz@huawei.com
Daniel King
Lancaster University
Email: d.king@lancaster.ac.uk
Authors' Addresses
Cheng Zhou
China Mobile
Beijing
China
Email: zhouchengyjy@chinamobile.com
Hongwei Yang
China Mobile
Beijing
China
Email: yanghongwei@chinamobile.com
Xiaodong Duan
China Mobile
Beijing
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
Nanjing
210012
China
Email: bill.wu@huawei.com
Mohamed Boucadair
Orange
Rennes
France
Email: mohamed.boucadair@orange.com
Christian Jacquenet
Orange
Rennes
France
Email: christian.jacquenet@orange.com
Datatracker
draft-irtf-nmrg-network-digital-twin-arch-06
This is an older version of an Internet-Draft whose latest revision state is "Active".
| Document | Document type |
This is an older version of an Internet-Draft whose latest revision state is "Active".
This document is an Internet-Draft (I-D) that has been submitted to the Internet Research Task Force (IRTF) stream.
This I-D is not endorsed by the IETF and has no formal standing in the
IETF standards process.
|
|
|---|---|---|---|
| Select version | |||
| Compare versions | |||
| Authors | Cheng Zhou , Hongwei Yang , Xiaodong Duan , Diego Lopez , Antonio Pastor , Qin Wu , Mohamed Boucadair , Christian Jacquenet | ||
| Replaces | draft-zhou-nmrg-digitaltwin-network-concepts | ||
| RFC stream |
|
||
| Other formats | |||
| Additional resources | Mailing list discussion |