Internet-Draft Network Working Group October 2021
Zhu, et al. Expires 28 April 2022 [Page]
Workgroup:
Internet Research Task Force
Internet-Draft:
draft-zhu-nmrg-digitaltwin-data-collection-01
Published:
Intended Status:
Informational
Expires:
Authors:
Y. Zhu
China Mobile
D. Chen
China Mobile
C. Zhou
China Mobile
P. Martinez-Julia, Ed.
NICT

An Efficient Data collection method for Digital Twin Network

Abstract

Digital Twin Network 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 time-lag, insufficient storage resources, low computational efficiency and waste of bandwidth resources. This document introduces an efficient data collection, aggregation and correlation method in which the Twin Network sends instructions to the Physical Network to collect data on demand, and then the Physical Network completes instructions such as knowledge representation, Telemetry Streaming Element of Physical Network completes data aggregation and correlation. Finally Telemetry Streaming Element sends the processed data to the 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.

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This Internet-Draft will expire on 28 April 2022.

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, we consider 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 Digital Twin Network 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 is needed, especially for complex scenarios that require real-time data and traffic from multi-source heterogeneous devices. Therefore, it is extremely important to propose a lightweight and efficient data collection, aggregation and correlation method.

2. Definitions and Acroyms

PN: Physical Network

IMC: Instruction Management Center

DSC: Data Storage Center

TN: Twin Network

TSE: Telemetry Streaming Element

RDF: Resource Description Framework

CPE: Complex Event Processing

3. Overview

Digital Twin Network 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 time-lag, 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 Digital Twin Network. This data collection method is to sends 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 representation data to the Digital Twin Network.

Digital Twin Network consists of Physical Network and Twin Network. The Physical Network includes multiple Data Storage Centers and Telemetry Streaming Element[I-D.ietf-opsawg-ntf], and the Twin Network includes the Instruction Management Center and Data Storage Center. Telemetry Streaming Element has multiple functions, including data collection, data aggregation, data correlation, knowledge representation and query, etc. In addition, the Complex Event Processing(CPE) engineer is integrated into TSE to perform query function. 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 in 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 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 Telemetry Streaming Element 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, log, traffic scheduling, business requirements, etc. On the other hand, it has the function of automatically parsing the instructions sent by the Telemetry Streaming Element. 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. Knowledge representation refers to the representation of the original data as a data structure that can be used for efficient computation. Such representation results are closer to machine language, which is conducive to the rapid and accurate construction of the model. 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 closer to the machine language, which is conducive to the rapid and accurate construction of the model.

+------------------------------+   +-----------------------+
|   Physical  Network          |   |  Digital Twin Network |
| +-----+    +-----+  +------+ |   |  +------+  +-------+  |
| |     |    |     |  |      | |   |  |      |  |       |  |
| | DSC |... | DSC |  | TSE  | |   |  |  IMC |  |  DSC  |  |
| |     |    |     |  |      | |   |  |      |  |       |  |
| +-+---+    +--+--+  +---+--+ |   |  +---+--+  +----+--+  |
|   |           |         |    |   |      |          |     |
+------------------------------+   +-----------------------+
    |           |         |               |          |
    | 1.1register         |               |          |
    +-----------+--------->               |          |
    |           |         |               |          |
    |           |1.2register              |          |
    |           +--------->               |          |
    |           |         | 1.3register   |          |
    |           |         +--------------->          |
    |           |         |            2.data request|
    |           |         |               <----------+
    |           |         |   3.query and instruction|
    |           |         |   configuration          |
    |           |         |               |          |
    |           |         4.send instruction         |
    |           |         <---------------+          |
    |           |         |               |          |
    |           |   5.parse and execute   |          |
    |           |       instruction       |          |
    |  6.data subscription|               |          |
    <---------------------+               |          |
    | 7.knowledge         |               |          |
    | representation      |               |          |
    |    8.data pushing   |               |          |
    +--------------------->               |          |
    |           |  9.data aggregation and |          |
    |           |     correlation         |          |
    |           |         |10.send processed data    |
    |           |         +-------------------------->
    |           |         |               |          |

The specific process is as follows:

  • The Data Storage Centers in the Physical Network registers with the Telemetry Streaming Element in the Physical Network. The Telemetry Streaming Element registers with the Instruction management center. The registration information includes the IP address of the Data Storage Center, the data type, the data source, or the data size, etc.
  • The Data Storage Center in the Twin Network sends the data collection request to the Instruction Management Center.
  • According to the data collection request, the Instruction Management Center intelligently query the registration information for addressing, and configures the data processing instruction.
  • The Instruction Management Center in the Twin Network sends the corresponding instruction according to the query result to the Telemetry Streaming Element in the Physical Network.
  • After receiving the instructions, the Telemetry Streaming Element in the Physical Network will parse them and execute them according to the instructions, and query the location of data stored. The query function can be performed by the Complex Event Processing (CEP) engine, which receives all telemetry data and processes it with all queries provided.
  • The Telemetry Streaming Element sends data subscription to DSC of the Physical Network.
  • DSC of Physical Network performs knowledge representation of local data, for example, in RDF form, also sends raw data to TSE for knowledge representation.
  • DSC of Physical Network push data or knowledge to TSE.
  • TSE aggregates and correlates the collected data or knowledge. Then according to the actual needs, decide whether to perform knowledge representation.
  • TSE sends the processed data or knowledge to DSC of Twin Network.

4. Conclusion

This memo introduces an efficient data collection method for Digital Twin Network. This data collection method is to sends 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 representation data to the Digital Twin Network. And the data collection process between the Physical Network and the Twin Network is introduced in detail.

6. IANA Considerations

This document has no requests to IANA.

7. References

7.1. Normative References

[RFC2119]
Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, , <https://www.rfc-editor.org/info/rfc2119>.

7.2. Informative References

[I-D.ietf-opsawg-ntf]
Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and A. Wang, "Network Telemetry Framework", Work in Progress, Internet-Draft, draft-ietf-opsawg-ntf-09, , <https://www.ietf.org/archive/id/draft-ietf-opsawg-ntf-09.txt>.
[I-D.zhou-nmrg-digitaltwin-network-concepts]
Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu, Q., Boucadair, M., and C. Jacquenet, "Digital Twin Network: Concepts and Reference Architecture", Work in Progress, Internet-Draft, draft-zhou-nmrg-digitaltwin-network-concepts-04, , <https://www.ietf.org/archive/id/draft-zhou-nmrg-digitaltwin-network-concepts-04.txt>.

Index

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Authors' Addresses

Yanhong Zhu
China Mobile
Beijing
100053
China
Danyang Chen
China Mobile
Beijing
100053
China
Cheng Zhou
China Mobile
Beijing
100053
China
Pedro Martinez-Julia (editor)
NICT
4-2-1, Nukui-Kitamachi, Koganei, Tokyo,
184-8795
Japan