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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].

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
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   Internet-Drafts are draft documents valid for a maximum of six months
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   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 January 13, 2021.

Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.





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   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.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

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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