Internet DRAFT - draft-zhu-nmrg-digitaltwin-data-collection
draft-zhu-nmrg-digitaltwin-data-collection
Internet Research Task Force Y. Zhu
Internet-Draft D. Chen
Intended status: Informational C. Zhou
Expires: 28 April 2022 China Mobile
P. Martinez-Julia, Ed.
NICT
25 October 2021
An Efficient Data collection method for Digital Twin Network
draft-zhu-nmrg-digitaltwin-data-collection-01
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.
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-
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Internet-Drafts are draft documents valid for a maximum of six months
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This Internet-Draft will expire on 28 April 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definitions and Acroyms . . . . . . . . . . . . . . . . . . . 3
3. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 7
5. Security Considerations . . . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Normative References . . . . . . . . . . . . . . . . . . 8
7.2. Informative References . . . . . . . . . . . . . . . . . 8
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 8
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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
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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,
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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.
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+------------------------------+ +-----------------------+
| 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.
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* 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.
5. Security Considerations
TBD.
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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, March 1997,
<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, 13 October 2021,
<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, 7 July 2021,
<https://www.ietf.org/archive/id/draft-zhou-nmrg-
digitaltwin-network-concepts-04.txt>.
Index
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Section 3
Authors' Addresses
Yanhong Zhu
China Mobile
Beijing
100053
China
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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
Pedro Martinez-Julia (editor)
NICT
4-2-1, Nukui-Kitamachi, Koganei, Tokyo,
184-8795
Japan
Email: pedro@nict.go.jp
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