Network Management Research Group C. Zhou
Internet-Draft H. Yang
Intended status: Informational X. Duan
Expires: January 13, 2021 China Mobile
July 12, 2020
Concepts of Digital Twin Network
draft-zhou-nmrg-digitaltwin-network-concepts-00
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].
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This Internet-Draft will expire on January 13, 2021.
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document authors. All rights reserved.
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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 . . . . . . 4
3.3. High efficient for network innovation . . . . . . . . . . 5
4. Challenges to build Digital Twin Network . . . . . . . . . . 5
5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6. Security Considerations . . . . . . . . . . . . . . . . . . . 7
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 7
8.1. Normative References . . . . . . . . . . . . . . . . . . 7
8.2. Informative References . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
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-
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
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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
Digitla 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
four key elements: data, mapping, model and interface, as shown in
Figure 1.
+--------------+
| |
| Interface |
| |
+-----+--------------+-----+
| |
| Analyze, Diagnose |
+------------+ +------------+
| | +----------------------+ | |
| Models | | NETWORK DIGITAL TWIN | | Data |
| | +----------------------+ | |
+------------+ +------------+
| Simulate, Control |
| |
+-----+--------------+-----+
| |
| Mappping |
| |
+--------------+
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.
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.
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o Standardized interface is the key technique enabler, which can
effectively ensure the compatibility and scalability of DTN
system.
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.
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.
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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
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.
4. 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
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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.
5. 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.
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6. Security Considerations
TBD.
7. IANA Considerations
This document has no requests to IANA.
8. References
8.1. 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>.
8.2. 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-01 (work in
progress), March 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
Hongwei Yang
China Mobile
Beijing 100053
China
Email: yanghongwei@chinamobile.com
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Xiaodong Duan
China Mobile
Beijing 100053
China
Email: duanxiaodong@chinamobile.com
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