An Efficient Data collection method for Digital Twin Network
draft-zhu-nmrg-digitaltwin-data-collection-00
The information below is for an old version of the document.
| Document | Type |
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 | ||
| RFC stream | (None) | ||
| Formats | |||
| Additional resources | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | I-D Exists | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
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.
Zhu, et al. Expires January 9, 2022 [Page 1]
Internet-Draft Network Working Group July 2021
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-
Zhu, et al. Expires January 9, 2022 [Page 2]
Internet-Draft Network Working Group July 2021
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
Zhu, et al. Expires January 9, 2022 [Page 3]
Internet-Draft Network Working Group July 2021
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.
Zhu, et al. Expires January 9, 2022 [Page 4]
Internet-Draft Network Working Group July 2021
+------------------------+ +--------------------------+
| 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.
Zhu, et al. Expires January 9, 2022 [Page 5]
Internet-Draft Network Working Group July 2021
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
Zhu, et al. Expires January 9, 2022 [Page 6]
Internet-Draft Network Working Group July 2021
[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
Zhu, et al. Expires January 9, 2022 [Page 7]