Concepts of Digital Twin Network
draft-zhou-nmrg-digitaltwin-network-concepts-02
The information below is for an old version of the document.
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| Authors | Cheng Zhou , Hongwei Yang , Xiaodong Duan , Diego Lopez , Antonio Pastor | ||
| Last updated | 2020-11-16 (Latest revision 2020-11-02) | ||
| Replaced by | draft-irtf-nmrg-network-digital-twin-arch, draft-irtf-nmrg-network-digital-twin-arch | ||
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draft-zhou-nmrg-digitaltwin-network-concepts-02
Internet Research Task Force C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: May 20, 2021 China Mobile
D. Lopez
A. Pastor
Telefonica I+D
November 16, 2020
Concepts of Digital Twin Network
draft-zhou-nmrg-digitaltwin-network-concepts-02
Abstract
Digital twin technology is becoming a hot technology in industry 4.0.
The application of digital twin technology in network field helps to
realize efficient and intelligent management and network innovation.
This document presents an overview of the concepts of Digital Twin
Network (DTN), provides the definition and DTN, and then describes
the benefits and key challenges of DTN.
Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
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
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This Internet-Draft will expire on May 20, 2021.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Definition of Digital Twin Network . . . . . . . . . . . . . 3
3. Benefits of Digital Twin Network . . . . . . . . . . . . . . 4
3.1. Lower the cost of network optimization . . . . . . . . . 4
3.2. More intelligent for network decision making . . . . . . 5
3.3. High efficient for network innovation . . . . . . . . . . 5
3.4. Privacy and Regulatory Compliance . . . . . . . . . . . . 6
3.5. Customize Network Operation Training . . . . . . . . . . 6
4. Reference Architecture of Digital Twin Network . . . . . . . 6
5. Challenges to build Digital Twin Network . . . . . . . . . . 9
6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 10
9.1. Normative References . . . . . . . . . . . . . . . . . . 10
9.2. Informative References . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
With the advent of 5G, Internet of Things and Cloud Computing, the
scale of network is expanding constantly. Accordingly, the network
operation and maintenance are becoming more complex due to higher
complexity of network; and innovations on network will be more and
more difficult due to the higher risk of network failure and higher
trial cost.
Digital twin is the real-time representation of physical entities in
the digital world. It has the characteristics of virtual-reality
integration and real-time interaction, iterative operation and
optimization, as well as full life-cycle, and full business data-
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driven. At present, it has been successfully applied in the fields
of intelligent manufacturing, smart city, complex system operation
and maintenance [Tao2019].
A digital twin network platform can be built by applying digital twin
technology to network and creating virtual image of physical network
facilities. Through the real-time data interaction between physical
network and twin network, the digital twin network platform can help
the network to achieve more intelligent, efficient, safe and full
life-cycle operation and maintenance.
2. Definition of Digital Twin Network
So far, there is no standard definition of digital twin network in
networking industry or SDOs. This document attempts to define
Digital Twin Network (DTN) as a virtual representation of the
physical network, analyzing, diagnosing, simulating and controlling
the physical network based on data, model and interface, so as to
achieve the real-time interactive mapping between physical network
and virtual twin network. According to the definition, DTN contains
five key elements: data, mapping, model, interface and orchestration
stack, as shown in Figure 1.
+------------+
| |
| Interface |
+------------+ | | +------------+
| |------+------------+------| |
| Models | | Data |
| | Analyze, Diagnose | |
+------------+ +------------+
| +----------------------+ |
| | NETWORK DIGITAL TWIN | |
| +----------------------+ |
| |
| Simulate, Control |
| |
+-------------+--------------+--------------+
| | | |
| Mapping | |Orchestration |
| | | |
+-------------+ +--------------+
Figure 1: Key Elements of Digital Twin Network
o Data is cornerstone for constructing a DTN system, in which
unified data repository can be the single source of the truth and
provide timely and accurate data support.
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o Real-time interactive mapping between physical network and virtual
twin network is the most typical feature that DTN is different
from network simulation system.
o Data model is the ability source of DTN. Various data models can
be designed and flexibly combined to serve various network
applications.
o Standardized interface is the key technique enabler, which can
effectively ensure the compatibility and scalability of DTN
system.
o The orchestration stack controls the flows of data and control
actions. It relies on the dynamic lifecycle management of network
models and elements to provide repeatablity (the capacity to
replicate network conditions on demand) and reproducibility (the
ability to replay successions of events, possibly under controlled
variations).
3. Benefits of Digital Twin Network
DTN can help enable closed-loop network management across the entire
lifecycle, from digital deployment and simulation, to visualized
assessment, physical deployment, and continuous verification. In
doing so, customers are able to achieve network-wide insights,
precise planning, and rapid deployment in multiple areas, including
networks, services, users, and applications. All the benefits of DTN
can be categorized into three major types: low cost of network
optimization, intelligent network decision making, and high efficient
network innovation. The following sections describe the three types
of benefits respectively.
3.1. Lower the cost of network optimization
With extremely large scale, network is becoming more and more complex
and difficult to operate. Since there is no effective platform for
simulation, traditional network optimization has to be tried on real
network directly with long time cost and high service impact running
on real network. This also greatly increases network operator's
OpEX.
With DTN platform, network operators can well simulate the candidate
optimization solutions before finally deploy them to real network.
Compared with traditional methods, this is of quite low risk and will
bring much less impact on real network. In addition, the operator's
OpEX will be greatly decreased accordingly.
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3.2. More intelligent for network decision making
Traditional network operation and management mainly focus on
deploying and managing current services, while lacking of handling
past data and predicting future status. This kind of passive and
protective maintenance is difficult to adapt to large-scale network
scenarios.
DTN can combine data acquisition, big data processing and AI modeling
to achieve the assessment of current status, diagnosis of past
problems, as well as prediction of future trends, then give the
results of analysis, simulate various possibilities, and provide more
comprehensive decision support. This will help network achieve
predictive maintenance from current protective maintenance. The
network behavioral repeatability and reproducibility properties in
the DTN allow to evaluate different conditions and controlled
variations of them, exploring choice as many times as needed to apply
the better emulation and decision procedures.
3.3. High efficient for network innovation
Due to higher trial risk, real network environment is normally
unavailable to network researcher when they explore innovation
techniques. Instead, researchers have to use some offline simulation
platforms. This greatly impacts the real effectiveness of the
innovation, and greatly slow down the speed of network innovation.
Moreover, risk-averse network operators naturally reluctant to try
new technologies due to higher failure risk as well as the higher
failure cost.
DTN can generate virtual twin entity of the real network. This helps
researches explore network innovation (e.g. new network protocols,
network AI/ML applications, etc.) efficiently, and helps network
operators 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%) of network and the slow
learning speed or phase-in learning steps of AI/ML algorithms. With
DTN platform, AI/ML can fully complete the leaning and training with
the sufficient data before deploy the model to the real network.
This will greatly encourage more network AI innovations in future
network.
Implementing Intent-Based Networking (IBN) via DTN can be another
example to show how DTN improves the efficiency of deploying network
innovation. IBN is an innovative technology for life-cycle network
management. Future network 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
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devices. [I-D.irtf-nmrg-ibn-concepts-definitions] clarifies the
concept of "Intent" and provides an overview of IBN functionalities.
The key character of an IBN system is that user's intent can be
assured automatically via continuously adjusting the policies and
validating the real-time situation. To lower the impact on real
network, several rounds of adjustment and validation can be simulated
on the DTN platform instead of directly on physical netowrk.
Therefore, DTN can be an important enabler platform to implement IBN
system and speed up the deployment of IBN in customer's network.
3.4. Privacy and Regulatory Compliance
The requirements on data confidentiality and privacy on network
service providers increase the complexity of network management, as
intelligent decision engines depend on data flows. As a result, the
improvement of data-enabled management requires complementary
techniques providing strict control and security mechanisms to
guarantee data privacy protection and regulatory compliance in these
aspects. Some examples of these techniques can include payload
inspection, including de-encryption user explicit consents, or data
anonymization mechanisms.
Given DTN works with mapped traffic or services from real networks,
but using traffic simulations, including automated tools for
synthetic user activity. The lack of personal data permits to lower
the privacy requirements and simplify privacy-preserving techniques,
as the data is not coming from real users. As a result, DTN allows
to focus on management improvements, without other concerns.
Additionally, logging and auditing the DTN experiments and synthetic
user activities provide additional information for further design and
planning, without the need of traffic inspection.
3.5. Customize Network Operation Training
Networks architectures can be complex, and their operation and
management require expert personnel and the learning curve can be
steep in most cases. DTN offers an opportunity to train staff for
customized networks and specific user needs. Several areas can
benefit with the use of it. 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.
4. Reference Architecture of Digital Twin Network
So far, there is no reference or standard architecture for Digital
Twin Network in network domain. Based on the definition of key
elements of DTN described in section 2, reference architecture with
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three layers of Digital Twin Network can be designed as below, shown
in Figure 2.
+---------------------------------------------------------+
| +-------+ +-------+ +-------+ Network|
| | App 1 | | App 2 | ... | App n | Application|
| +-------+ +-------+ +-------+ |
+-------------^-------------------+-----------------------+
| ability supply |intent input
| |
+---------------------------------v-----------------------+
| Network Digital Twin|
| +--------+ +------------------------+ +--------+ |
| | | | Service Mapping Models | | | |
| | | | +------------------+ | | | |
| | Data +---> |Functional Models | +---> Digital| |
| | Sharing| | +-----+-----^------+ | | Twin | |
| | Repo- | | | | | | Entity | |
| | sitory | | +-----v-----+------+ | | Mngmt | |
| | <---+ | Basic Models | <---+ | |
| | | | +------------------+ | | | |
| +--------+ +------------------------+ +--------+ |
+--------^------------------------------------------------+
| |
| data collection | control
+-------------------------------------v-------------------+
| Physical Network|
| Network infrastructures |
+---------------------------------------------------------+
Figure 2: Reference Architecutre of Digital Twin Network
1. Bottom layer is Physical Network. All network elements in
physical network exchange massive network data and control with
network digital twin entity, via southbound interfaces. Physical
network can be either telecommunication operator network, or data
center network, campus network, industrial Internet of things or
other network types.
2. Middle layer is Network Digital Twin Entity, which is the core of
DTN system. This layer includes three key subsystems: Data
Sharing Repository, Service Mapping Models and Digital Twin
Entity Management.
* Data Sharing Repository provides accurate and complete
information for building various service models by collecting
and updating the real-time operational data of various network
elements through the southbound interface. In addition to
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data storage, Data Sharing Repository is also responsible to
provide data services for the Service Mapping Models sub-
system, including fast retrieval, concurrent conflict, batch
service, unified interface, etc.
* Service Mapping Models completes data-based modelling,
provides data model instances for various network
applications, and maximizes the agility and programmability of
network services. The data models include two major types:
basic models and functional models.
+ Basic Model refers to the network element model and network
topology model of the network digital twin entity based on
the basic configuration, environment information,
operational state, link topology and other information of
the network element, to complete the real-time accurate
description of the physical network.
+ Functional model refers to various data models such as
network analysis, simulation, diagnosis, prediction,
assurance, etc. The functional models can be constructed
and expanded by multiple dimensions: by network type, there
can be models serving for single network domain or multi
network domain; by function type, it can be divided into
state monitoring, traffic analysis, security drill, fault
diagnosis, quality assurance and other models; by
generality, it can be divided into general model and
special-purpose model. Specifically, multiple dimensions
can be combined to create a data model for more specific
application scenario.
* Digital Twin Entity Management completes the management
function of digital twin network, records the life-cycle of
the entity, visualizes and controls various elements of
network digital twin, including topology management, model
management and security management.
3. Top layer is Network Application. Various applications (e.g.
Network intelligent O&M, IBN, etc.) can effectively run against
Digital Twin Network platform to implement either conventional or
innovative network operations, with low cost and less service
impact on real network. Network application provide requirements
to network digital twin entity via northbound interface; then the
service is simulated by various service model instances; after
fully verified, the change control can be deployed safely to
physical network.
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5. Challenges to build Digital Twin Network
As mentioned in above section, DTN can bring many benefits to network
management as well as network innovation. However, it is still
challenging to build an effective and efficient DTN system. The
following are the major challenges and problems.
o Large scale challenge: The digital twin entity of large-scale
network will significantly increase the complexity of data
acquisition and storage, the design and implementation of model.
And the requirements of software and hardware of the system will
be very high.
o Compatibility issue: It is difficult to establish a unified
digital twin platform with unified data model in the whole network
domain due to the inconsistency of technical implementation and
supporting functionalities of different manufacturers' devices in
the network.
o Data modeling difficulties: Based on large-scale network data,
data modeling should not only focus on ensuring the richness of
model functions, but also need to consider the flexibility and
scalability of the model. These requirements further increase the
difficulty of building efficient and hierarchical functional data
models.
o Real-time requirement: For services with high real-time
requirements, the processing of model simulation and verification
through DTN system will increase the service delay, so the
function and process of the data model need to increase the
processing mechanism under various network application scenarios;
at the same time, the real-time requirements will further increase
the system software and hardware performance requirements.
o Security risks: Network digital twin entity synchronizes all the
data of physical network in real time, which will increase the
security risk of user data, such as information leakage or more
vulnerable to attack.
To solve the above problems and challenges, Digital Twin Network
needs continuous optimization and breakthrough on key enabling
technologies including data acquisition, data storage, data modeling,
network visualization, interface standardization, and security
assurance, so as to meet the requirements of compatibility,
reliability, real-time and security under large-scale network.
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6. Summary
The research and application of Digital Twin Network is just
beginning. This document presents an overview of the concepts and
definition of DTN. Looking forward, further researches on DTN usage
scenarios, requirements, architecture and key enabling technologies
should be promoted by the industry, so as to accelerate the
implementation and deployment of DTN in real network.
7. Security Considerations
TBD.
8. IANA Considerations
This document has no requests to IANA.
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
9.2. Informative References
[I-D.irtf-nmrg-ibn-concepts-definitions]
Clemm, A., Ciavaglia, L., Granville, L., and J. Tantsura,
"Intent-Based Networking - Concepts and Definitions",
draft-irtf-nmrg-ibn-concepts-definitions-02 (work in
progress), September 2020.
[Tao2019] Tao, F., Zhang, H., Liu, A., and A. Nee, "Digital Twin in
Industry: State-of-the-Art. IEEE Transactions on
Industrial Informatics, vol. 15, no. 4.", April 2019.
Authors' Addresses
Cheng Zhou
China Mobile
Beijing 100053
China
Email: zhouchengyjy@chinamobile.com
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Hongwei Yang
China Mobile
Beijing 100053
China
Email: yanghongwei@chinamobile.com
Xiaodong Duan
China Mobile
Beijing 100053
China
Email: duanxiaodong@chinamobile.com
Diego Lopez
Telefonica I+D
Seville
Spain
Email: diego.r.lopez@telefonica.com
Antonio Pastor
Telefonica I+D
Madrid
Spain
Email: antonio.pastorperales@telefonica.com
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