ETSI GR ENI 009 V1.1.1 (2021-06)

Experiential Networked Intelligence (ENI); Definition of data processing mechanisms

DGR/ENI-0017-Data_Process_Mech

General Information

• Status: Not Published
• Technical Committee: ENI - Experiential Networked Intelligence
• Current Stage: 12 - Completion
• Due Date: 14-Jun-2021
• Completion Date: 08-Jun-2021

Standards Content (Sample)

ETSI GR ENI 009 V1.1.1 (2021-06)






GROUP REPORT
Experiential Networked Intelligence (ENI);
Definition of data processing mechanisms
Disclaimer
The present document has been produced and approved by the Experiential Networked Intelligence (ENI) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

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Reference
DGR/ENI-0017-Data_Process_Mech
Keywords
artificial intelligence, data collection, data
management, data mechanism, data processing,
data storage, network management

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Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
Introduction . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definition of terms, symbols and abbreviations . 8
3.1 Terms . 8
3.2 Symbols . 9
3.3 Abbreviations . 10
4 Overview . 11
4.1 Background . 11
4.2 Data Precondition . 11
5 Data Mechanism . 11
5.1 Introduction . 11
5.1.1 Data Mechanism Overview. 11
5.2 Data Characteristics . 12
5.2.1 Configuration Data . 12
5.2.2 Sequential Data . 12
5.2.3 Data Format . 13
5.3 Data Source . 13
5.3.1 Introduction. 13
5.4 Data Collection . 14
5.4.1 Introduction. 14
5.4.2 Data Acquisition Modes . 14
5.4.3 Data Collection Techniques . 15
5.4.3.1 Introduction . 15
5.4.3.2 Data carried out in functional planes protocols . 15
5.4.3.2.1 Data carried out in the Forwarding/User Plane . 15
5.4.3.2.2 Data carried out in the Control Plane . 15
5.4.3.2.3 Data carried out in the Management Plane . 16
5.4.3.3 Specific data used to deploy telemetry . 16
5.4.3.3.1 Network Telemetry . 16
5.4.3.3.2 Resource Telemetry . 18
5.4.3.3.3 Fault Telemetry . 19
5.4.3.3.4 Streaming Telemetry . 20
5.5 Hierarchical data storage . 21
5.6 Data Processing . 21
5.6.1 Data Correlation . 21
5.6.2 Data Cleansing . 22
5.7 Data Sharing . 22
5.8 Data Management. 23
5.8.1 Overview . 23
5.8.2 Metadata Management . 23
5.8.3 Data Security Management . 23
5.8.4 Data Quality Management . 23
6 Example Scenarios to Illustrate Data Mechanisms . 23
6.1 AI-enabled Traffic Classification Use Case . 23
6.1.1 Introduction. 23
6.1.2 Data Acquisition . 23
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6.1.3 Data Processing for traffic classification . 24
6.2 Network Fault Root-Cause Analysis and Intelligent Recovery Use Case . 24
6.2.1 Introduction. 24
6.2.2 Data Acquisition . 24
6.2.3 Data Processing . 25
6.3 Intelligent Service Experience Evaluation Use Case . 25
6.3.1 Introduction. 25
6.3.2 Data Acquisition . 26
7 Recommendations . 26
Annex A: Change History . 28
History . 29


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Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Experiential Networked
Intelligence (ENI).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
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Introduction
The present document outlines a high-level reference framework that describes technical methods for producing
high-quality actionable data efficiently and in a timely manner.
The organization of the present document is as follows:
• Clause 1 defines the scope of the present document.
• Clauses 2 and 3 provide informative references, terms, symbols and abbreviations.
• Clause 4 describes an overview of the data mechanism, including its motivation and challenges.
• Clause 5 defines components in the high-level framework of the data mechanism in terms of data acquiring
and data processing.
• Clause 6 presents the data mechanisms in some example scenarios proposed in ETSI GR ENI 001 [i.1], Use
Case specification.
• Clause 7 concludes possible contributions to other ENI group specifications of the present document.
Data Telemetry is used as an example for data mechanisms description and analysis.

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1 Scope
The present document describes some technical methods to support data-driven intelligent network scenarios. The
realization of intelligent networks depend on extracting value from Big Data using AI algorithms. Therefore, effective
data acquisition, processing and management is extremely important as described in this context.
The present document covers the following aspects:
1) Data classification in terms of the data sources producing the data (e.g. network management system, network
elements, servers, terminals and external environment data), data characteristics (e.g. configuration or
sequential data), and data format.
2) Data operation including data collection, data storage, data processing, data sharing and data management:
a) Data collection including description about collection modes (e.g. pull (query/request response) and push
(publish/notify)), and data collection techniques, such as telemetry.
b) Data storage recommendations.
c) Data processing, including data cleansing and data correlation.
d) Data sharing.
e) Data management, including metadata management, data security management and data quality
management.
3) Data acquisition and processing methods of selected use cases proposed in ETSI GR ENI 001 [i.1] for ENI
systems executing intelligent tasks.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GR ENI 001 (V3.1.1): "Experiential Networked Intelligence (ENI); ENI use cases".
[i.2] ETSI GR ENI 004 (V3.1.1): "Experiential Networked Intelligence (ENI); Terminology for Main
Concepts in ENI".
[i.3] ETSI GS ENI 005 (V2.1.1): "Experiential Networked Intelligence (ENI); System Architecture".
[i.4] IETF RFC 7011: "Specification of the IP Flow Information Export (IPFIX) Protocol for the
Exchange of Flow Information".
[i.5] IETF RFC 7950: "The YANG 1.1 Data Modeling Language".
[i.6] IETF RFC 4656: "A One-way Active Measurement Protocol (OWAMP)".
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[i.7] IETF RFC 5357: "A Two-Way Active Measurement Protocol (TWAMP)".
[i.8] IETF I-D.ietf-ippm-ioam-data-11: "Data Fields for In-situ OAM".
[i.9] IETF RFC 8321: "Alternate-Marking Method for Passive and Hybrid Performance Monitoring".
[i.10] IETF RFC 8889: "Multipoint Alternate Marking method for passive and hybrid performance
monitoring".
[i.11] IETF RFC 7799: "Active and Passive Metrics and Methods (with Hybrid Types In-Between)".
[i.12] Recommendation ITU-T Y.1731: "OAM functions and mechanisms for Ethernet based networks".
[i.13] IETF RFC 6241: "Network Configuration Protocol (NETCONF)".
[i.14] IETF RFC 4271: "A Border Gateway Protocol 4 (BGP-4)".
[i.15] IETF RFC 7854: "BGP Monitoring Protocol (BMP)".
[i.16] IETF I-D.draft-kumar-rtgwg-grpc-protocol-00: "gRPC Protocol".
[i.17] IETF I.D.draft-zhou-ippm-enhanced-alternate-marking-05: "Enhanced Alternate Marking
Method".
[i.18] IETF I.D.draft-song-ippm-postcard-based-telemetry-08: "Postcard-based On-Path Flow Data
Telemetry using Packet Marking".
[i.19] IETF RFC 793: "Transmission Control Protocol (TCP)".
[i.20] IETF RFC 768: "User Datagram Protocol (UDP)".
[i.21] VNF Event Stream (VES).
NOTE: Available at https://wiki.opnfv.org/display/ves/VES+Home.
[i.22] IETF RFC 3416: "Version 2 of the Protocol Operations for the Simple Network Management
Protocol (SNMP)".
[i.23] IETF RFC 959: "File Transport Protocol (FTP)".
[i.24] The Atlan Data wiki definition of structured data.
NOTE: Available at https://wiki.atlan.com/structured-data/.
[i.25] The Atlan Data wiki definition of unstructured data.
NOTE: Available at https://wiki.atlan.com/unstructured-data/.
[i.26] IETF RFC 4560: "Definitions of Managed Objects for Remote Ping, Traceroute, and Lookup
Operations".
[i.27] Prometheus open source.
NOTE: Available at: https://prometheus.io/.
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in ETSI GR ENI 004 [i.2], ETSI GS ENI 005 [i.3] and the
following apply:
column-oriented database: database that organizes data by field
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NOTE: This type of database keeps all of the data associated with a field next to each other in memory, and is
optimized for online analytical processing. They are optimized for reading and computing on columnar
data. Examples include Snowflake and BigQuery.
data lake: centralized storage repository that stores raw data that are in the form of structured, semi-structured and
unstructured format
data mart: subset of a data warehouse focused on a particular line of business, department, or subject area
data warehouse: repository used to connect, analyse, and report on historical and current data from heterogeneous
sources
NOTE: A data warehouse is designed for query and analysis as opposed to transaction processing. It analyses and
reports on data from operational systems as used in decision-support systems.
hadoop distributed file system: distributed fault-tolerant file system that stores data on commodity machines and
provides high throughput access
massively parallel processing: use of a large number of processing nodes that perform a set of coordinated tasks in
parallel using a high-speed network
NOTE: The processing nodes typically are independent, and do not share memory, and typically each node runs
its own instance of an operating system.
Prometheus: open-source systems monitoring and alerting toolkit
NOTE: This open source is originally built at SoundCloud. Since its inception in 2012, many companies and
organizations have adopted Prometheus, and the project has a very active developer and user community.
It is now a standalone open source project and maintained independently of any company. To emphasize
this, and to clarify the project's governance structure, Prometheus joined the Cloud Native Computing
Foundation in 2016 as the second hosted project, after Kubernetes.
protocol buffers (protobuf): language-neutral, platform-neutral, extensible mechanism for serializing structured data
reinforcement learning: See ETSI GR ENI 004 [i.2] and ETSI GS ENI 005 [i.3].
row-oriented database: database that organizes data by record
NOTE: This type of database keeps all of the data associated with a record next to each other in memory, and is
optimized for online transaction processing. An example is MySQL.
semi-structured data: information that does not conform to a formal data model, but does have some organizational
properties that define key data (e.g. tags) that enable data to be self-describing
software defined hardware: software programmable hardware that is able to be reconfigured at runtime to enable near
ASIC performance without sacrificing programmability for data-intensive algorithms
structured data: information organized in a predetermined way (a fixed format, data model or schema) within a record
or a file
NOTE 1: As defined in [i.24].
NOTE 2: Structured data enables all elements to be individually addressable, and conform to a data model.
unstructured data: information that does not have a pre-defined data model, and does not contain properties that
provide any organization or structure to its elements
NOTE: Unstructured data needs to be processed in order to find information by domain-specific applications.
video stalling: process during the video playback, the video is paused and waits for the buffer due to dragging or other
reasons
3.2 Symbols
Void.
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3.3 Abbreviations
For the purposes of the present document, the abbreviations given in ETSI GR ENI 004 [i.2], ETSI GS ENI 005 [i.3]
and the following apply:
5G Fifth Generation
AI Artificial Intelligence
AS Autonomous System
BGP Border Gateway Protocol
BMP BGP Monitoring Protocol
BSS Business Support Systems
CPU Central Processing Unit
CRM Customer Relationship Management
EAM Explicit Address Mapping
ENI Experiential Networked Intelligence
FTP File Transport Protocol
gNMI gRPC Network Management Interface
IETF Internet Engineering Task Force
IMS Integrated Management System
IOAM In-band OAM
IP Internet Protocol
IPFIX IP Flow Information eXport
IPFPM IP Flow Performance Measurement
IPPM IP Performance Metrics
ITU International Telecommunication Union
ITU-T ITU Telecommunication standardization sector
JSON JavaScript Object Notation
KPI Key Performance Indicator
MS Monitoring System
NE Network Element
NMS Network Management System
OAM Operation, Administration and Maintenance
OMC Operations and Maintenance Centre
OSS Operations Support Systems
OWAMP One-Way Active Measurement Protocol
PBT Postcard-Based Telemetry
QoS Quality of Service
SDN Software-Defined Networking
SDK Software Development Kit
SLA Service Level Agreement
SNMP Simple Network Management Protocol
SQL Structured Query Language
SR-IOV Single Root I/O Virtualization
TCP Transmission Control Protocol
TWAMP Two-Way Active Measurement Protocol
UDP User Datagram Protocol
VES VNF Event Stream
VNF Virtual Network Function
XML Extensible Markup Language
YANG Yet Another Next Generation
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4 Overview
4.1 Background
Exploiting network data for intelligent network applications and use has been increasing in recent years. By combining
AI and machine learning algorithms, network data is able to provide insights that help network operators better manage
and optimize the network. Therefore, the quality of available sample data, for instance, time validity, diversity, volume,
accuracy, plays an important role in learning from data. One challenge is that large amounts of data as well as data that
meets the demands is able to be acquired. Additionally, the data collected from network equipments from different
vendors varies in the aspect of name, format, calculation rules, etc. Thus a large amount of time is often be spent to do
the data normalizing, cleansing, and engineering before those data could be used to train the model. This blocks the
deployment of actionable decisions, which are meant to improve ENI System performance and User Experience.
The present document describes data acquisition, sharing and processing mechanisms, as well as supports for data
privacy in AI-enabled network Operation, Administration and Management (OAM). The present document identifies
the sources and data to be extracted, however it does not intend to explain how the mechanisms work, or how data is
processed in order to became used. This could be addressed in a later release.
4.2 Data Precondition
Different types of data are able to be analysed only and interpreted correctly in particular contexts. The following are
examples of some of the types of data that the present document focuses on.
Real-time data: Typically, network data has to be continually monitored and dynamically processed in real-time.
Example processing includes filtering, correlation, and cleansing. This is typically down locally and then aggregated
results are distributed for further processing.
Continuous data: In some cases, continuous data over a long time span is required for analysis or model training. For
example, historical traffic data are used to predict future traffic trends. In general, the longer the time span, the more
representative it is, but the larger the data volume. Therefore
...