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Concepts of Digital Twin Network

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This is an older version of an Internet-Draft whose latest revision state is "Replaced".
Authors Cheng Zhou , Hongwei Yang , Xiaodong Duan , Diego Lopez , Antonio Pastor , Qin Wu , Mohamed Boucadair , Christian Jacquenet
Last updated 2021-02-22 (Latest revision 2020-11-16)
Replaced by draft-irtf-nmrg-network-digital-twin-arch
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Internet Research Task Force                                     C. Zhou
Internet-Draft                                                   H. Yang
Intended status: Informational                                   X. Duan
Expires: August 26, 2021                                    China Mobile
                                                                D. Lopez
                                                               A. Pastor
                                                          Telefonica I+D
                                                                   Q. Wu
                                                            M. Boucadair
                                                            C. Jacquenet
                                                       February 22, 2021

                    Concepts of Digital Twin Network


   Digital Twin technology has been seen as a rapid adoption technology
   in Industry 4.0.  The application of Digital Twin technology in the
   telecommunications field is meant to realize efficient and
   intelligent management and accelerate 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 such technology.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119][RFC8174] when, and only when, they appear in all
   capitals, as shown here.

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   This Internet-Draft is submitted in full conformance with the
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   time.  It is inappropriate to use Internet-Drafts as reference
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   This Internet-Draft will expire on August 26, 2021.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definition of Digital Twin Network  . . . . . . . . . . . . .   3
   3.  Benefits of Digital Twin Network  . . . . . . . . . . . . . .   5
     3.1.  Lower the Cost of Network Optimization  . . . . . . . . .   5
     3.2.  Optimized Decision Making . . . . . . . . . . . . . . . .   6
     3.3.  Safer Assessment of Innovative Network Capabilities . . .   6
     3.4.  Privacy and Regulatory Compliance . . . . . . . . . . . .   6
     3.5.  Customize Network Operation Training  . . . . . . . . . .   7
   4.  Reference Architecture of Digital Twin Network  . . . . . . .   7
   5.  Challenges to build Digital Twin Network  . . . . . . . . . .   9
   6.  Interaction with IBN  . . . . . . . . . . . . . . . . . . . .  10
   7.  Application Scenarios . . . . . . . . . . . . . . . . . . . .  10
     7.1.  Human Training  . . . . . . . . . . . . . . . . . . . . .  10
     7.2.  ML Training . . . . . . . . . . . . . . . . . . . . . . .  11
     7.3.  DevOps-oriented certification . . . . . . . . . . . . . .  11
     7.4.  Network fuzzing . . . . . . . . . . . . . . . . . . . . .  11
   8.  Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .  11
   9.  Open Issues . . . . . . . . . . . . . . . . . . . . . . . . .  12
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  12
   11. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  13
   12. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   13. References  . . . . . . . . . . . . . . . . . . . . . . . . .  13
     13.1.  Normative References . . . . . . . . . . . . . . . . . .  13
     13.2.  Informative References . . . . . . . . . . . . . . . . .  13
   Appendix A.  Change Logs  . . . . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

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1.  Introduction

   With the advent of technologies such as 5G, Industrial Internet of
   Things, Edge Computing, and Artificial Intelligence (AI), the ICT
   industry and other vertical industries such as smart city or smart
   manufacturers are transformed dramatically through replacing what is
   used to be manual processes with digital processes.

   With the fast growing of the network scale and the increased demand
   placed on the network driven by end user, accommodating and adapting
   dynamically to customer needs becomes a big challenge to network
   operators.  Indeed, network operation and maintenance are becoming
   more complex due to higher complexity of the managed network.  As
   such, providing innovations on network will be more and more
   difficult due to the higher risk of network failure and higher trial
   cost if no reliable emulation platforms are available.

   Digital Twin is the real-time representation of physical entities in
   the digital world.  It has the characteristics of virtual-reality
   interrelation and real-time interaction, iterative operation and
   process 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, or complex system
   operation and maintenance [Tao2019] to help with not only object
   design and test, but also operation and maintenance.

   A digital twin network platform can be built by applying Digital Twin
   technology to network and creating virtual image of physical network
   facilities (emulation).  Through the real-time data interaction
   between the physical network and its twin network, the digital twin
   network platform might help the network designers to achieve more
   simplification, automatic, resilient, and full life-cycle operation
   and maintenance.  Having an emulation platform that allows to
   reliably represent the state of a network is more reliable than a
   simulation platform.  The emulated platform can thus be used to
   assess specific behaviors before actual implementation in the
   physical network, tweak the network for better optimized behavior,
   run 'what-if' scenarios that can't be tested and evaluated easily in
   the physical network.

2.  Definition of Digital Twin Network

   There is no standard definition of digital twin network in networking
   industry or SDOs.  This document attempts to define Digital Twin
   Network as a virtual representation of the physical network.  Such
   virtualized representation of the network is meant to analyze,
   diagnose, emulate, and control the physical network.  To that aim,
   real-time and interactive mapping is required between the between

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   physical network and the virtual twin network.  Digital Twin Network
   may involve five key elements: data, mapping, model, interface, and
   orchestration stack as shown in Figure 1.

               +-------------+                 +--------------+
               |             |                 |              |
               |  Mapping    |                 |Orchestration |
               |             |                 |              |
                        |                          |
                        |    Analyze, Diagnose     |
                        |                          |
                        | +----------------------+ |
                        | | NETWORK DIGITAL TWIN | |
                        | +----------------------+ |
            +------------+                        +------------+
            |            |   Simulate, Control    |            |
            |   Models   |                        |    Data    |
            |            |-----+------------+-----|            |
            +------------+     |            |     +------------+
                               | Interface  |
                               |            |

              Figure 1: Key Elements of Digital Twin Network

   Data:  Provide a unified data repository aggregated from multiple
      data sources in the network, can be the single source of the
      "truth" and provide timely and accurate data search support.

   Data Model:  An abstract model that organizes elements of data.
      Various data models such as YANG data models, database models, or
      knowledge graph can be designed to represent the physical network
      assets and flexibly trimmed or interwoven to serve various network

   Interface:  Standardized interfaces include telemetry interface
      between Network Digital Twin Platform and Physical Network
      Infrastructure, data as a service interface between Network
      Digital Twin Platform and Application and can effectively check
      the data inconsistency and ensure compatibility and scalability of
      DTN system.

   Mapping:  Different from the traditional network simulation system,
      it provides real-time interactive mapping between physical network
      and virtual twin network, which emulate the behavior of a network
      by calculating the deviation between the different network
      entities (routers, switches, nodes, access points, links etc.) in

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      the physical network and corresponding entities in the virtual
      twin network.

   Orchestration:  Two kind or orchestration are provided, one is to
      controlling the DTN environment and its components to derive the
      required behavior.  The second is to deal with the dynamic
      lifecycle management of these components.  The second
      orchestration provides repeatability (the capacity to replicate
      network conditions on demand) and reproducibility (the ability to
      replay successions of events, possibly under controlled

3.  Benefits of Digital Twin Network

   Digital Twin Networks 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, network operators (and end-users to some
   extent) can get a global, systemic and consistent view of the
   network.  Network operators can also safely assess the enforcement of
   network planning policies, deployment procedures, etc., without
   jeopardizing the daily operation of the physical network.  The
   benefits of DTN can be classified into: low cost of network
   optimization, optimized and safer decision-making, safer testing of
   innovative network capabilities (including "what if"
   scenarios),Privacy and Regulatory Compliance and Customize Network
   Operation Training.  The following sections detail such benefits.

3.1.  Lower the Cost of Network Optimization

   Large scale networks are complex to operate.  Since there is no
   effective platform for simulation, network optimization designs have
   to be tested on the physical 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 OpEX budgets too.

   With a Digital Twin Network platform, network operators can safely
   emulate candidate optimization solutions before deploying them in the
   physical network.  In addition, the operator's OpEX on the real
   physical network deployment will be greatly decreased accordingly at
   the cost of the complexity of the assessment and the resources

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3.2.  Optimized Decision Making

   Traditional network operation and management mainly focus on
   deploying and managing current services, but hardly support
   predictive maintenance techniques.

   DTN can combine data acquisition, big data processing and AI modeling
   to assess the status of the network, but also to predict future
   trends, and better organize predictive maintenance.  The DTN's
   ability to reproduce network behaviors under various conditions
   facilitates the corresponding assessment of the various evolution
   options as often as required.

3.3.  Safer Assessment of Innovative Network Capabilities

   Testing a new feature in an operational network is not only complex:
   it's also extremely risky.

   DTNs can thus greatly help assessing innovative network capabilities
   without jeopardizing the daily operation of the physical network.  In
   addition, it also helps researches explore network innovation (e.g.
   new network protocols, network AI/ML applications, etc.) efficiently,
   and 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
   learning 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.

3.4.  Privacy and Regulatory Compliance

   The requirements on data confidentiality and privacy on network
   service providers increase the complexity of network management, as
   decisions made by computation logics such as a SDN controller may
   rely upon the contents of 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.  Some examples of
   these techniques include payload inspection, including de-encryption
   user explicit consents, or data anonymization mechanisms.

   Given DTN operation assumes the mapping between real traffic or
   services and the traffic used by the DTN for assessment purposes in
   particular, the need for privacy is of the utmost importance.  The
   lack of personal data permits to lower the privacy requirements and
   simplifies the use of privacy-preserving techniques.

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3.5.  Customize Network Operation Training

   Network architectures can be complex, and their operation requires
   expert personnel.  DTN 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 the threat
   detection and mitigation.

4.  Reference Architecture of Digital Twin Network

   So far, there is no reference or standard DTN architecture.  Based on
   the definition of the key DTN elements introduced in section 2, a DTN
   architecture that relies upon three layers is depicted in Figure 2.

        |   +-------+   +-------+          +-------+       Network|
        |   | App 1 |   | App 2 |   ...    | App n |   Application|
        |   +-------+   +-------+          +-------+              |
                      |Capability Exposure|intent input
                      |                   |
        |                                     Network Digital Twin|
        |  +--------+   +------------------------+   +--------+   |
        |  |        |   | Service Mapping Models |   |        |   |
        |  |        |   |  +------------------+  |   |        |   |
        |  | Data   +--->  |Functional Models |  +---> Digital|   |
        |  | Repo-  |   |  +-----+-----^------+  |   | Twin   |   |
        |  | sitory |   |        |     |         |   | Entity |   |
        |  |        |   |  +-----v-----+------+  |   |  Mgmt  |   |
        |  |        <---+  |  Basic Models    |  <---+        |   |
        |  |        |   |  +------------------+  |   |        |   |
        |  +--------+   +------------------------+   +--------+   |
                 |                            |
                 | data collection            | control
        |                   Physical Network                      |
        |                                                         |

         Figure 2: Reference Architecture of Digital Twin Network

   1.  The lowest layer is Physical Network.  All network elements in
       physical network exchange massive network data and control with
       network digital twin entity, via southbound interfaces.

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   2.  The Intermediate layer is the Network Digital Twin Entity, which
       is the core of the DTN system.  This layer includes three key
       subsystems: Data Repository, Service Mapping Models and Digital
       Twin Entity Management.

       *  Data Repository provides accurate and complete information
          about the network and its components 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 data storage, the
          Repository is also responsible for providing data search
          services to the Service Mapping Models sub-system, including
          fast retrieval, concurrent conflict, batch service, unified
          interface, etc.

       *  Service Mapping Models completes data modellling, provides
          data model instances for various network capabilities, 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 a single or multiple network
             domains; by function type, it can be divided into state
             monitoring, traffic analysis, security exercise, fault
             diagnosis, quality assurance and other models; it can also
             be divided into general model and special-purpose model.
             Specifically, multiple dimensions can be combined to create
             a data model for more specific application scenarios.

       *  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 the
          network digital twin, including topology management, model
          management and security management.

   3.  Top layer is Network Application.  Various applications (e.g.
       OAM, IBN, etc.) can effectively run over a Digital Twin Network

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       platform to implement either conventional or innovative network
       operations, with low cost and less service impact on real
       networks.  Network applications raise requirements that need to
       be addressed by the DTN.  Such requirements are exchanged through
       a northbound interface; then the service is emulated by various
       service model instances; once checked, changes can be safely
       deployed in the physical network.

5.  Challenges to build Digital Twin Network

   As mentioned in the above section, DTNs can bring many benefits to
   network management as well as facilitate the introduction of
   innovative network capabilities.  However, building an effective and
   efficient DTN system remains a challenge.  The following is a list of
   the major challenges.

   o  Large scale challenge: The digital twin entity of large-scale
      networks will significantly increase the complexity of data
      acquisition and storage, the design and implementation of models.
      And the requirements of software and hardware of the system will
      be even more constraining.

   o  Compatibility issue: It is difficult to establish a unified
      digital twin platform with a unified data model in the whole
      network domain due to the inconsistency of technical
      implementations and the heterogeneity of vendor technologies.

   o  Data modeling difficulties: Based on large-scale network data,
      data modeling should not only focus on ensuring the accuracy of
      model functions, but also need to consider the flexibility and
      scalability of the model.  Balancing these requirements further
      increase the complexity of building efficient and hierarchical
      functional data models.

   o  Real-time requirement: For services with real-time requirements,
      the processing of model simulation and verification through a DTN
      system will increase the service delay, so the function and
      process of the data model need to be based on automated processing
      mechanism under various network application scenarios; at the same
      time, the real-time requirements will further increase performance
      requirements on the system software and hardware.

   o  Security risks: the DTN synchronizes all the data of physical
      networks in real time, which inevitably augments the attack
      surface, with a higher risk of information leakage in particular.

   To address these challenges, the Digital Twin Network needs
   continuous optimization and breakthrough on key enabling technologies

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   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.

6.  Interaction with IBN

   Implementing Intent-Based Networking (IBN) via DTN can be an 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 characteristic 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 network.
   Therefore, DTN can be an important enabler platform to implement IBN
   system and speed up the deployment of IBN in customer's network.

7.  Application Scenarios

   Digital Twin Network can be applied to solve different problems in
   network management and operation.

7.1.  Human Training

   The usual approach to network Operations, Administration, and
   Maintenance (OAM) with procedures applied by humans is open to errors
   in all these procedures, with impact in network availability and
   resilience.  Response procedures and actions for most relevant
   operational requests and incidents are commonly defined to reduce
   errors to a minimum.  The progressive automation of these procedures,
   such as predictive control or closed loop management, reduce the
   faults and response time, but still there is the need of a human-in-
   the-loop for multiples actions.  These processes are not intuitive
   and require training to learn how to respond.  The use of DTN 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.  DTN will offer
   realistic environments, fitted to the real production networks.

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7.2.  ML Training

   Machine Learning requires data and their context to be available in
   order to apply it.  A common approach in the network management
   environment has been to simulate or import data in a specific
   environment (the ML developer lab), where they are used to train the
   selected model, while later, when the model is deployed in
   production, re-train or adjust to the production environment context.
   This demands a specific adaption period.  DTNs simplify 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 DTN and not
   to the production network, allowing information access by third
   parties, without impacting data privacy.

7.3.  DevOps-oriented certification

   The potential application of CI/CD models network management
   operations increases the risk associated to deployment of non-
   validated updates, what conflicts with the goal of the certification
   requirements applied by network service providers.  A solution for
   addressing these certification requirements is to verify the specific
   impacts of updates on service assurance and SLAs using a DTN
   environment replicating the network particularities, as a previous
   step to production release.  DTN orchestration capacities support the
   dynamic mechanisms required by DevOps procedures.

7.4.  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
   implies higher risk to errors or vulnerabilities in software and
   configuration.  DTN allows to apply fuzzing testing techniques on a
   twin network environment, with interactions and conditions similar to
   the production network, permitting to identify and solve
   vulnerabilities, bugs and zero-days attacks before production

8.  Summary

   Research on Digital Twin Networks has just started.  This document
   presents an overview of the DTN concepts.  Looking forward, further
   elaboration on DTN scenarios, requirements, architecture and key
   enabling technologies should be promoted by the industry, so as to
   accelerate the implementation and deployment of DTNs.

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9.  Open Issues

   o  Why distinguish data from model?  Typically data repository can
      store data models.

   o  Why is Digital Twin Network components separated from the
      orchestration component?  Should Digital Twin Network components
      part of orchestration?

   o  Do we need to first show the interfaces between the physical
      network and its twin and then focus on the twin part with the
      various required components to build the twin image?

   o  Which component is responsible for checking for deviation of the
      underlay network vs. the image?

   o  Is continuous verification an implicit reference to CI/CD
      procedures where the DTN would be used to run non-regression tests
      (for example) before deploying a major release?  Please be more

10.  Security Considerations

   This document describes concepts and definitions of Digital Twin
   Network.  As such, the below security considerations remain high
   level, i.e. in the form of principles, guidelines or requirements.

   Security in the Digital-Twin network can apply to the following

   o  Secure the digital twin system itself.

   o  Data privacy protection

   Securing the digital twin system aims at making the digital-twin
   system operationally secure by implementing security mechanisms and
   applying security best practices.  In the context of digital-twin
   Network, such mechanisms and practices may consist in data
   verification and model validation; mapping operations between
   physical network and digital counterpart network by authenticated and
   authorized users only.

   Synchronizing all the data between physical network and Network
   digital twin entity may increase the risk of sensitive data and
   information leakage.  Strict control and security mechanisms such as
   payload inspection can be provided to mitigate data privacy risk.

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11.  Acknowledgements

   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).

12.  IANA Considerations

   This document has no requests to IANA.

13.  References

13.1.  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,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

13.2.  Informative References

              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.

Appendix A.  Change Logs

   v02 - v03

   o  Split interaction with IBN part as a separate section.

   o  Fill security section;

   o  Clarify the motivation in the introduction section;

   o  Use new boilerplate for requirements language section;

   o  Key elements definition update.

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   o  Other editorial changes.

   o  Add open issues section.

   o  Add section on application scenarios.

Authors' Addresses

   Cheng Zhou
   China Mobile
   Beijing  100053


   Hongwei Yang
   China Mobile
   Beijing  100053


   Xiaodong Duan
   China Mobile
   Beijing  100053


   Diego Lopez
   Telefonica I+D


   Antonio Pastor
   Telefonica I+D


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   Qin Wu
   101 Software Avenue, Yuhua District
   Nanjing, Jiangsu  210012


   Mohamed Boucadair
   Rennes 35000


   Christian Jacquenet
   Rennes 35000


Zhou, et al.             Expires August 26, 2021               [Page 15]