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An Efficient Data collection method for Digital Twin Network
draft-zhu-nmrg-digitaltwin-data-collection-00

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This is an older version of an Internet-Draft whose latest revision state is "Replaced".
Authors Yanhong Zhu , Danyang Chen , Cheng Zhou
Last updated 2021-07-09
Replaced by draft-zcz-nmrg-digitaltwin-data-collection
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draft-zhu-nmrg-digitaltwin-data-collection-00
Internet Research Task Force                                      Y. Zhu
Internet-Draft                                                   D. Chen
Intended status: Informational                                   C. Zhou
Expires: January 9, 2022                                    China Mobile
                                                            July 8, 2021

      An Efficient Data collection method for Digital Twin Network
             draft-zhu-nmrg-digitaltwin-data-collection-00

Abstract

   Digital Twin Network (DTN) is a network system with Physical Network
   and Twin Network, which can be mapped interactively in real time.
   The construction of Digital Twin Network requires real-time data of
   Physical Network to update the state of Twin Network.  However the
   existing method collects the full amount of data from the Physical
   Network for modeling, and does not consider the problems such as
   insufficient storage resources, low computational efficiency and
   waste of bandwidth resources.  This document introduces an efficient
   data collection method in which the Twin Network sends instructions
   to the Physical Network to collect data on demand, and then the
   Physical Network parses and executes instructions such as data
   cleaning and knowledge representation, and sends the processed or
   requested data to the Digital Twin Network.

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
   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 January 9, 2022.

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Copyright Notice

   Copyright (c) 2021 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.  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.  Definitions and Acroyms . . . . . . . . . . . . . . . . . . .   3
   3.  Overview  . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   6
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   With the deployment of Internet of Things, cloud computing and data
   center, etc., the scale of the current network is expanded gradually.
   However, the increase of network scale leads to the increasing
   complexity of the current network, and that induces plenty of
   problems.  In order to improve the autonomy ability of network and
   reduce the negative effect on Physical Network, it is considered that
   an endogenous intelligent and autonomous network architecture which
   achieves self-optimization and decision is indispensable.  Digital
   twin, as an innovative technology, has the potential to realize this
   architecture because it can optimize and validate policies through
   real-time and interactive mapping with physical
   entities.[I-D.zhou-nmrg-digitaltwin-network-concepts]

   Data is the cornerstone of DTN construction.  In the face of large
   network scale, data collection, storage and management are faced with
   great challenges.  If the full-data collection method is adopted,
   huge storage space and bandwidth resource are needed, especially for
   complex scenarios that require real-time data and traffic from multi-

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   source heterogeneous devices.  Therefore, it is extremely important
   to propose a lightweight and efficient data collection method.

2.  Definitions and Acroyms

   DTN: Digital Twin Network

   PN: Physical Network

   IMC: Instruction Management Center

   DSC: Data Storage Center

   TN: Twin Network

3.  Overview

   Digital Twin Network (DTN) is a network system with Physical Network
   and Twin Network, which can be mapped interactively in real time.
   The construction of DTN requires real-time data of Physical Network
   to update the state of Twin Network.  However the existing method
   collects the full amount of data from the Physical Network for
   modeling, and does not consider the problems such as insufficient
   storage resources, low computational efficiency and waste of
   bandwidth resources caused by data transmission.  In order to solve
   these problems, this memo introduces an efficient data collection
   method for DTN.  This data collection method is to send instructions
   in the Twin Network to the Physical Network to collect data on
   demand, and then the Physical Network parses and executes
   instructions such as data cleaning or knowledge representation, and
   then sends the processed or represented data to the DTN.

   DTN consists of Physical Network and Twin Network.  The Physical
   Network includes multiple Data Storage Centers, and the Twin Network
   includes the Instruction Management Center and Data Storage Center.
   The Instruction Management has two functions.  On the one hand, the
   Instruction Management Center of the Twin Network is mainly used to
   manage the registration of the Data Storage Center in the Physical
   Network, and its registration information can include various key
   information such as the IP address of the Data Storage Center in the
   Physical Network, data type, and various index names of the data ,
   data source name and data size, etc; on the other hand, it is mainly
   used to adaptively configure data collection instructions according
   to the collection requirements of the Data Storage Center in the Twin
   Network, and search for IP addresses to send instructions.  The
   instruction-carrying information includes rule-based mathematical
   expressions, executable models in .exe format, dynamic collection
   frequency, parameter lists, program text files in .m format, text

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   files with parameter configuration, and other types of files.
   Instructions are flexible and programmable, and can be created,
   modified, combined, and deleted at any time according to
   requirements.  When the Data Storage Center of the Twin Network
   initiates data collection requests to the Instruction Management
   Center, the Instruction Management Center searches for IP addresses
   of Data Storage Center from registration information according to
   critical information such as data type and data name, and functional
   instructions for data processing or knowledge representation can be
   implemented depending on the demand configuration.  The Data Storage
   Center of the Twin Network is mainly used to store the effective
   information after data processing and knowledge representation
   returned by the Data Storage Center in the Physical Network.

   Data Storage Center in the Physical Network has two functions.  On
   the one hand, it can store data, such as performance indicators,
   operational status, logs, traffic scheduling, business requirements,
   etc.  On the other hand, it has the function of automatically parsing
   the instructions sent by the Instruction Management Center in the
   Twin Network.  Then the operating environment of the instruction is
   configured according to the instruction needs, and data processing or
   knowledge representation is performed based on the instruction.  Data
   processing mainly includes data cleaning, filling missing data,
   normalization, conflict verification, etc.  The role of knowledge
   representation is to represent the original data as a data structure
   that can be used to efficiently calculate.  Such representation
   results are closer to the machine language, which is conducive to the
   rapid and accurate construction of the model.

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   +------------------------+     +--------------------------+
   |   Physical  Network    |     |   Digital Twin Network   |
   | +-------+    +-------+ |     | +-----------+  +-------+ |
   | | Data  |    | Data  | |     | |Instruction|  | Data  | |
   | |storage|... |storage| |     | |management |  |storage| |
   | |center |    | center| |     | |center     |  | center| |
   | +---+---+    +---+---+ |     | +---+-------+  +------++ |
   |     |            |     |     |     |                 |  |
   +-----+------------+-----+     +-----+-----------------+--+
         |            |                 |                 |
         |            |                 |                 |
         |            |                 |                 |
         |            |                 |                 |
         |            |  1.1 register   |                 |
         +------------+----------------->                 |
         |            |                 |                 |
         |            |  1.2 register   |                 |
         |            +----------------->                 |
         |            |                 | send data       |
         |            |                 |  request        |
         |            |                 <-----------------+
         |            |                 |                 |
         |            |                 +-+3configuration |
         |            |                 | |  instructions |
         | 4 send instructions according+-v               |
         |    to the address            |                 |
         <------------+-----------------+                 |
         |     5 parse|and              |                 |
         +----+   execute               |                 |
         |    | instruction             |                 |
         +----v       |                 |                 |
         |            |                 |                 |
         | 6 send processed data and knowledge            |
         +------------+-----------------+-----------------+
         |            |                 |                 |
         |            |                 |                 |

   The specific process is as follows:

   o  The Data Storage Centers in the Physical Network register to the
      Instruction Management Center in the Twin Network.  The
      registration information includes the IP address of the Data
      Storage Center, the data type, the data source, the data size,
      etc.

   o  The Data Storage Center in the Twin Network sends the data
      collection request to the Instruction Management Center.

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   o  According to the data collection request, the Instruction
      Management Center intelligently searches the registration
      information for addressing, and configures the data processing
      instruction.  The instruction-carrying information includes rule-
      based mathematical expressions, executable models in .exe format,
      dynamic collection frequency, parameter lists, program text files
      in .m format, text files with parameter configuration, and other
      types of files.  And these are created, modified, combined and
      deleted flexibly according to requirements

   o  The Instruction Management Center in the Twin Network sends the
      corresponding instruction according to the address to the Data
      Storage Center in the Physical Network.

   o  After receiving the instructions, the Data Storage Center in the
      Physical Network will parse and execute them according to the
      instructions, such as filling missing data, data association,
      knowledge representation, etc.

   o  The Data Storage Center of the Physical Network will send the
      processed and represented data to the Data Storage Center in the
      Twin Network.

4.  Conclusion

   This memo introduces an efficient data collection method for DTN.
   This data collection method is to send instructions model in the Twin
   Network to the Physical Network to collect data on demand, and then
   the Physical Network completes instructions such as data cleaning or
   knowledge representation, and then sends the processed and
   represented data to the DTN.  With this method, DTN can build and
   maintain it's data porosity more efficiently and effectively.

5.  Security Considerations

   TBD.

6.  IANA Considerations

   This document has no requests to IANA.

7.  References

7.1.  Normative References

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   [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>.

7.2.  Informative References

   [I-D.zhou-nmrg-digitaltwin-network-concepts]
              Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
              Q., Boucadair, M., and C. Jacquenet, "Concepts of Digital
              Twin Network", draft-zhou-nmrg-digitaltwin-network-
              concepts-03 (work in progress), February 2021.

Authors' Addresses

   Yanhong Zhu
   China Mobile
   Beijing  100053
   China

   Email: zhuyanhong@chinamobile.com

   Danyang Chen
   China Mobile
   Beijing  100053
   China

   Email: chendanyang@chinamobile.com

   Cheng Zhou
   China Mobile
   Beijing  100053
   China

   Email: zhouchengyjy@chinamobile.com

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