Internet DRAFT - draft-zcz-nmrg-digitaltwin-data-collection
draft-zcz-nmrg-digitaltwin-data-collection
Internet Research Task Force C. Zhou
Internet-Draft D. Chen
Intended status: Informational China Mobile
Expires: 10 January 2024 P. Martinez-Julia
NICT
Q. Ma
Huawei
9 July 2023
Data Collection Requirements and Technologies for Digital Twin Network
draft-zcz-nmrg-digitaltwin-data-collection-03
Abstract
A Digital Twin Network is a virtual representation of a real network,
which is meant to be used by a management system to analyze,
diagnose, emulate, and then control the real network based on data,
models, and interfaces. The construction and state update of a
Digital Twin Network require obtaining real-time information of the
physical network it represents (i.e., telemetry data). This document
aims to describe the data collection requirements and provide data
collection methods or tools to build the data repository for building
and updating a 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 10 January 2024.
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Copyright Notice
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 3
3. Data Collection Requirements for Digital Twin Network . . . . 4
3.1. Target-driven and On-demand Collection . . . . . . . . . 4
3.2. Diverse Tools for Various Data Collection . . . . . . . . 4
3.3. Lightweight and Efficient Collection . . . . . . . . . . 5
3.4. Open and Standardized Interfaces . . . . . . . . . . . . 5
3.5. Naming for Caching . . . . . . . . . . . . . . . . . . . 6
3.6. Efficient Multi-Destination Delivery . . . . . . . . . . 6
4. Data Collection Technologies for Digital Twin Network . . . . 6
4.1. Existing Data Collection Methods/Tools . . . . . . . . . 6
4.2. Innovation Directions on Data Collection . . . . . . . . 7
5. Knowledge and Instruction Driven Data Collection Method for
Digital Twin Network . . . . . . . . . . . . . . . . . . 8
5.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 8
5.2. Efficient Data Collection Mechanism . . . . . . . . . . . 8
5.3. Data Collection Process . . . . . . . . . . . . . . . . . 10
5.4. Query and Aggregation Functions . . . . . . . . . . . . . 11
6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7. Security Considerations . . . . . . . . . . . . . . . . . . . 13
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 13
9.1. Normative References . . . . . . . . . . . . . . . . . . 13
9.2. Informative References . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
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1. Introduction
With the deployment of Internet of Things (IoT), cloud computing and
data center, etc., the scale of the current network is expanded
gradually. However, the increase of network scale also leads to an
increase in the complexity of the current network, and it induces
plenty of problems. In order to improve the autonomy ability of
network and reduce potential negative effects on physical and virtual
networks, we consider that an endogenous intelligent and autonomous
network architecture which achieves self-optimization and decision is
indispensable (in general, self-management and self-operation). The
digital twin technology addresses the challenge of building self-
management systems because it can optimize and validate policies
through real-time and interactive mapping with physical entities
[I-D.irtf-nmrg-network-digital-twin-arch].
Data is the cornerstone required for constructing a digital twin for
a network, namely a Digital Twin Network (DTN). In the face of large
network scale, data collection, storage and management are faced with
great challenges. So, data collection methods and tools should meet
the requirements of target-driven, diversity, lightweight and
efficiency, while being open and standardized. Among all the
requirements, achieving a lightweight and efficient data collection
method is of the most importance. 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-source and heterogeneous devices. Therefore, it
is extremely important to agree on lightweight and efficient data
collection, aggregation, and correlation methods, toward building the
transmission of monitoring information (telemetry data), processing,
and storage required to build a DTN system.
This document aims to describe the data collection requirements and
propose efficient data collection methods or tools to build the data
repository for digital twin network.
2. Definitions and Acronyms
PN: Physical Network
IMC: Instruction Management Center
DSC: Data Storage Center
DTN: Digital Twin Network
TSE: Telemetry Streaming Element
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RDF: Resource Description Framework
CEP: Complex Event Processing
3. Data Collection Requirements for Digital Twin Network
3.1. Target-driven and On-demand Collection
The monitoring data of a network is the basis to build a DTN system.
Such data is collected from physical and virtual networks. It
includes, but is not limited to, the following types:
* Provisional and operational status of physical or virtual devices,
as well as the network topology with all network elements.
* Configuration data that is required to transform a network system
from its initial default state into its current state.
* Running status of physical, logical, or virtual ports and links.
* Logs and events records of all the network elements.
* Statistics (packet loss, traffic throughput, latency, etc.) of
flows and ports.
* Various data regarding users and services.
* Life-cycle operation data of all network elements.
* All above data in time series.
The collection of the monitoring information from a network required
for maintaining a DTN (telemetry data) should be in target-driven and
on-demand mode. It is not always necessary to collect all monitoring
information from the network (telemetry data) listed above because of
the high cost of resources (CPU, memory, bandwidth etc.). The type,
frequency and method of data collection aim to meet the application
of a DTN depend on the specific network topology and application
requirements.
3.2. Diverse Tools for Various Data Collection
The different types of monitoring information required to maintain a
DTN (telemetry data) have several characteristics. Some data (e.g.
hardware status, environmental data, etc.) requires lower collecting
frequency, while others (e.g. flow status, link fault, etc.) need
higher level of real-time. Some data (e.g. device status, port
statistics, etc.) can be collected directly and simply via normal
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tools, while others (e.g. per-flow latency, traffic matrix, etc.) can
only be acquired through complex network measurement technologies.
It is unrealistic to find or define a uniform data collection method
that is suitable for all types of data. Therefore, multiple tools or
methods are needed to collect the different types of data required to
build the DTN entity.
3.3. Lightweight and Efficient Collection
Data collection tools and methods should be as lightweight as
possible, so as to reduce the occupation of network equipment
resources and ensure that data collection does not affect the normal
operation of the network. The major requirements are listed as
below.
* Data collection tools and methods need to improve efficiency of
execution, reduce the cost of computing, storage and communication
bandwidth.
* The collection of redundant data should be avoided or minimized.
* For the data set that needs to be collected, making full use of
the data compression technology, to reduce the resource cost in
the collection phase. There must be lossy or lossless compression
methods available to data sources, which will be applied together
with other functions before data is transmitted.
3.4. Open and Standardized Interfaces
Data collection interfaces used to build the DTN should be open and
standardized to help avoid either hardware or software vendor lock,
and facilitate inter-operability among different vendors. The major
requirements of data collection interfaces are:
* Support configuration management, including the data collection
channel, frequency or period, etc.
* Support several rate options (e.g. minute-level, 10-second level,
second level (near real time), and millisecond-level) to
accommodate different data requirements from applications.
* Be extensible so that more features can be added in future with
limited parameter changes and with backward compatibility.
* Be able to provide secure and reliable information exchange
mechanism.
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* Be able to enforce federation policies to allow information to be
exchangeable among domains while ensuring authorization and
scoping is controlled.
3.5. Naming for Caching
Both raw monitoring information (telemetry data) and knowledge items
obtained from monitoring must be able to be addressed uniquely. This
means to give a unique identifier or "name" to each data or knowledge
item that references it. This name will be used by caching
mechanisms to store the data and provide it for clients that request
it, which will also use such name.
Global names and federated names must be supported. A name schema,
name hierarchy, and name part ontology must be defined and maintained
together with other naming systems, such as DNS for global names.
3.6. Efficient Multi-Destination Delivery
The maintenance of DTN systems will not be the sole purpose of
monitoring information and knowledge communication. Other
applications would also request raw monitoring information (telemetry
data) or knowledge items. They can use the name to identify it. The
monitoring system (telemetry system), following the recommendations
of RFC 9232 [RFC9232], will deliver the requested data or knowledge
items to the requesters as much efficiently as possible. On the one
hand, items will be provided by the closest cache to the destination
of the data. On the other hand, items will be replicated in the best
nodes, following an efficient multi-cast spanning tree. Different
underlying protocols can be used to achieve this mechanism.
Delivering knowledge items instead of raw telemetry data enables
digital twins to be aware of the context of data and highly relieve
from complex processing, which will be performed by the entities
which are best suited for running each type of processing.
4. Data Collection Technologies for Digital Twin Network
4.1. Existing Data Collection Methods/Tools
Currently, some widely-used tools, such as SNMP, RESTCONF [RFC8040],
NETCONF [RFC6241], Telemetry, INT (In-band Network Telemetry), DPI
(Deep Packet Inspection), IPFIX [RFC7011], etc. can be candidate
tools to collect data for digital twin network. YANG data model and
associated mechanisms defined in [RFC8639][RFC8641] enable
subscriber-specific subscriptions to a publisher's event streams, and
can help subscriber applications to request a continuous and
customized stream of updates from a YANG datastore. Appendix-A in
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[RFC9232] gives a survey on existing network telemetry techniques,
which explores an overview of management plane, control plane and
data plane telemetry techniques and standards.
Moreover, some new innovation methods can help increase the data
collection efficiency. For example,
[I-D.claise-opsawg-collected-data-manifest] proposes a YANG model to
store contextual information along with the collected data in order
to keep the collected data exploitable;
[I-D.ietf-ippm-explicit-flow-measurements] addresses the network
performance measurement problem under encrypted transport protocols,
via proposing some hybrid measurement methods based on marking bits
in packet headers without relying on external network management
systems. [RFC7594] introduces a measurement method named Large-Scale
Measurement of Broadband Performance (LMAP) that works in a
coordinated fashion to perform network performance measurement tasks.
4.2. Innovation Directions on Data Collection
Current data collection methods and tools (YANG, xCONF, SNMP,
Telemetry, etc.) listed above can help acquire network data to build
a Digital Twin Network system, which may be with low maturity and
low-level capabilities of data service and data modelling. To build
a more mature DTN system with high-level capabilities, it is
necessary to explore more innovative data collection technologies.
The following are several potential innovation directions.
* High-performance data collection technology based on programmable
circuits, which offer the potential for hardware acceleration and
customization.
* Measurement methods for complex monitoring information such as
network performance and network traffic.
* Distributed and collaborative data collection techniques for
integrating and fusing data from multiple data sources, and the
time synchronization problem of data acquisition.
* Provision of processed information, jointly and separately, by
applying the function indicated by data requester.
* Assessment of federation policies in data provisioning to enable
cross-domain data provision and implement multi-domain digital
twin scenarios.
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* Investigating self-adaptive and self-learning data collection
techniques that can dynamically adjust data collection parameters,
methods, and priorities based on network conditions and user
requirements.
* Exploring machine learning and AI techniques to enhance the
efficiency and accuracy of data collection processes by
identifying patterns, correlations and anomalies in network data.
5. Knowledge and Instruction Driven Data Collection Method for Digital
Twin Network
5.1. Overview
The DTN's data repository sub-system manages all network data, in
real time, from the PN to the DTN. Sufficient and timely data are
always required to construct the twin entity and various data models.
However the existing methods collect the full data from the PN for
modeling, and do not consider problems like time-lag, insufficient
storage resources, low computational efficiency and waste of
bandwidth resources caused by data transmission.
This section proposes an efficient data collection method, named
"knowledge and instruction driven data collection". This data
collection method is based on sending instructions to the elements of
the PN for them to pre-process the data (data cleaning or knowledge
representation) before sending it back to be applied to the DTN.
5.2. Efficient Data Collection Mechanism
The management system structure consists of the PN and the DTN. The
PN includes multiple Data Storage Centers (DSC) and Telemetry
Streaming Element (TSE), and the DTN includes the Instruction
Management Center (IMC) and Data Storage Center (DSC). The TSE has
multiple functions, including data collection, data aggregation, data
correlation, knowledge representation and query, etc. In addition, a
Complex Event Processing (CEP) engine is integrated into TSE to
perform queries to the streamed data. The IMC has two functions: one
is used to manage the registration of the DSC in the PN side, and its
registration information can include various key information such as
the IP address of the DSC in the PN side, choose data type, and
various index names in the data, data source name and data size, etc.
The other is used to adaptively configure data collection
instructions according to the collection requirements of the DSC in
the DTN side 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 DSC of the DTN side requests data to the IMC,
the IMC searches the IP address of the DSC in the database with the
registration information, which is built 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 DSC of the
DTN side stores the effective information after data processing and
knowledge representation returned by the TSE.
The DSC in the PN side has two functions. On the one hand, it stores
data of various types, 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 TSE. 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 similar 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.
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+------------------------------+ +-----------------------+
| Physical Network | | Digital Twin Network |
| +-----+ +-----+ +------+ | | +------+ +-------+ |
| | | | | | | | | | | | | |
| | DSC |... | DSC | | TSE | | | | IMC | | DSC | |
| | | | | | | | | | | | | |
| +-+---+ +--+--+ +---+--+ | | +---+--+ +----+--+ |
| | | | | | | | |
+------------------------------+ +-----------------------+
| | | | |
| 1.1. Register | | |
+-----------+---------> | |
| | | | |
| | 1.2. Register | |
| +---------> | |
| | | 1.3. Register | |
| | +---------------> |
| | | 2. Data req. |
| | | <----------+
| | | 3. Query and instruction |
| | | configuration |
| | | + |
| | 4. Send instructions |
| | <---------------+ |
| | | | |
| | 5. Parse and execute | |
| | instruction | |
| 6. Data subscript. | | |
<---------------------+ | |
| 7. Knowledge | | |
| representation | | |
| 8. Data pushing | | |
+---------------------> | |
| | 9. Data aggregation and | |
| | correlation | |
| | | 10. Send processed data |
| | +-------------------------->
| | | | |
Figure 1: Data Collection Process
5.3. Data Collection Process
The specific process is as follows:
* The DSC in the PN side registers into the TSE. The TSE registers
into the IMC. Both provide their IP addresses, the data type, the
data source, the data size, etc.
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* The DSC in the DTN side sends the data collection request to the
IMC.
* According to the data collection request, the IMC intelligently
queries the registration addressing information and configures the
data processing instruction.
* The IMC in the DTN side sends the corresponding instruction
according to the query result to the TSE.
* After receiving the instructions, the TSE parses them and executes
them. The query function can be performed by the CEP engine,
which receives all monitoring information (telemetry data) and
processes it with all queries provided.
* The TSE sends data subscription to DSC in the PN side.
* The DSC in the PN side represents the data semantically in RDF
form or sends the data in raw form to the TSE for it to make the
semantic representation.
* The DSC in the PN side pushes the data or knowledge item to the
TSE.
* The TSE aggregates and correlates the collected data or knowledge
items. Then, according to the actual needs, generates aggregated
data or knowledge items.
* The TSE sends the resulting data or knowledge items to the DSC in
the DTN side.
5.4. Query and Aggregation Functions
The TSE supports an arbitrary number of queries and aggregation
functions. As a minimum, it will support:
* A function to apply a particular calculation to the values
retrieved from a specified metric for a specified period of time.
The basically supported calculations must be:
- Average: Returns the single number resulting from averaging all
values in the period.
- Maximum: Returns the single number that represents the highest
value in the period.
- Minimum: Returns the single number that represents the lowest
value in the period.
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- Percentile X: Returns the percentile of calculated at position
X (from 0, which is the minimum, to 100, which is the maximum).
- Moving Average X: Transforms all values of the specified period
by calculating every value as the average of the previous X
values (or less if there are not enough).
- Filter Previous X: Removes the values that change less than X
percent from the previous value.
- Filter Average X: Removes the values that change less than X
percent from the average value.
- Filter Moving Average X Y: Removes the values that change less
than Y percent from the value of the moving average for X
previous values.
* A function to represent the collected values in a semantic
structure following some ontology, information model, and data
format (YANG). This will enforce semantic constraints to the
values, such as avoiding negative measures of some parameters
(e.g., bandwidth usage).
* A function to analyze the collected values to detect some pattern
(provided) and, if so, trigger some notification that other module
can use to execute some action.
The particular behavior of the three functions will be described in a
high-level language that is transformed to the specific code used by
the device, such as [P4].
6. Summary
This draft describes the requirements for data collection and
provides the data collection methods or tools required to build the
data repository for maintaining DTN systems. These data collection
methods or tools should meet the requirement of target-driven,
diversity, lightweight and efficiency, while being open and
standardized. Among all the requirements, lightweight and efficiency
requirements are the most important. Thus, this draft provides a
lightweight and efficient method for data collection that is
particularly optimized for maintaining DTN systems. Going forward,
more methods (transformation and aggregation functions) and tools
(solutions) shall be studied to extend the contents of this draft.
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7. Security Considerations
TBD.
8. IANA Considerations
This document has no requests to IANA.
9. References
9.1. 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>.
[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/info/rfc8639>.
[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/info/rfc8641>.
[RFC9232] Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
A. Wang, "Network Telemetry Framework", RFC 9232,
DOI 10.17487/RFC9232, May 2022,
<https://www.rfc-editor.org/info/rfc9232>.
9.2. Informative References
[I-D.claise-opsawg-collected-data-manifest]
Claise, B., Quilbeuf, J., Lopez, D., Martinez-Casanueva,
I. D., and T. Graf, "A Data Manifest for Contextualized
Telemetry Data", Work in Progress, Internet-Draft, draft-
claise-opsawg-collected-data-manifest-06, 10 March 2023,
<https://datatracker.ietf.org/doc/html/draft-claise-
opsawg-collected-data-manifest-06>.
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[I-D.ietf-ippm-explicit-flow-measurements]
Cociglio, M., Ferrieux, A., Fioccola, G., Lubashev, I.,
Bulgarella, F., Nilo, M., Hamchaoui, I., and R. Sisto,
"Explicit Host-to-Network Flow Measurements Techniques",
Work in Progress, Internet-Draft, draft-ietf-ippm-
explicit-flow-measurements-06, 6 July 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-ippm-
explicit-flow-measurements-06>.
[I-D.irtf-nmrg-network-digital-twin-arch]
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-irtf-nmrg-network-digital-
twin-arch-03, 27 April 2023,
<https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
network-digital-twin-arch-03>.
[P4] The P4 Language Consortium, "P4 Language Specification
(https://p4.org/p4-spec/docs/P4-16-v-1.2.3.html)", 11 July
2022.
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC7594] Eardley, P., Morton, A., Bagnulo, M., Burbridge, T.,
Aitken, P., and A. Akhter, "A Framework for Large-Scale
Measurement of Broadband Performance (LMAP)", RFC 7594,
DOI 10.17487/RFC7594, September 2015,
<https://www.rfc-editor.org/info/rfc7594>.
[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/info/rfc8040>.
Authors' Addresses
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Cheng Zhou
China Mobile
Beijing
100053
China
Email: zhouchengyjy@chinamobile.com
Danyang Chen
China Mobile
Beijing
100053
China
Email: chendanyang@chinamobile.com
Pedro Martinez-Julia
NICT
4-2-1, Nukui-Kitamachi, Koganei, Tokyo
184-8795
Japan
Email: pedro@nict.go.jp
Qiufang Ma
Huawei
Nanjing
210012
China
Email: maqiufang1@huawei.com
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