ETSI GR ENI 009 V1.2.1 (2023-05)

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

RGR/ENI-009v121_Data_Proc_Mech

General Information

• Status: Not Published
• Technical Committee: ENI - Experiential Networked Intelligence
• Current Stage: 12 - Completion
• Due Date: 09-May-2023
• Completion Date: 05-May-2023

Standards Content (Sample)

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.

2 ETSI GR ENI 009 V1.2.1 (2023-05)

Reference
RGR/ENI-009v121_Data_Proc_Mech
Keywords
artificial intelligence, data collection, data
management, data mechanism, data processing,
data storage, network management

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3 ETSI GR ENI 009 V1.2.1 (2023-05)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
Introduction . 5
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 Representation . 13
5.2.4 Data Exchange Formats . 13
5.2.5 Data Model from FBs in ENI System . 14
5.2.5.1 Data model types . 14
5.2.5.2 Data model template . 15
5.2.5.2.1 Introduction . 15
5.2.5.2.2 Common part of the data template. 15
5.2.5.2.3 Specific part of the data template . 15
5.3 Data Source . 18
5.3.1 Introduction. 18
5.4 Data Collection . 19
5.4.1 Introduction. 19
5.4.2 Data Acquisition Modes . 19
5.4.3 Data Collection Techniques . 19
5.4.3.1 Introduction . 19
5.4.3.2 Data carried out in functional planes protocols . 19
5.4.3.2.1 Data carried out in the Forwarding/User Plane . 19
5.4.3.2.2 Data carried out in the Control Plane . 20
5.4.3.2.3 Data carried out in the Management Plane . 20
5.4.3.3 Specific data used to deploy telemetry . 20
5.4.3.3.1 Network Telemetry . 20
5.4.3.3.2 Resource Telemetry . 21
5.4.3.3.3 Fault Telemetry . 23
5.4.3.3.4 Streaming Telemetry . 24
5.5 Hierarchical data storage . 24
5.6 Data Processing . 25
5.6.1 Data Correlation . 25
5.6.2 Data Cleansing . 26
5.7 Data Sharing . 26
5.8 Data Management. 27
5.8.1 Overview . 27
5.8.2 Metadata Management . 27
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5.8.3 Data Security Management . 27
5.8.4 Data Quality Management . 27
5.9 Data Conversion . 27
5.9.1 Introduction. 27
5.9.2 Data Conversion between Functional Blocks . 27
5.9.3 Data Conversion between ENI AI Data Model and External System . 28
5.9.3.1 Introduction to Data Conversion between ENI AI Data Model and External System . 28
5.9.3.2 Introduction to AI Data models . 28
5.9.3.3 AI Data model types. 29
5.9.3.4 The corresponding framework of AI Data model . 29
5.9.3.5 External system data conversion . 30
5.9.3.5.1 Introduction . 30
5.9.3.5.2 Converting structured data into vectors . 31
5.9.3.5.3 Converting semi-structured data into vectors . 31
5.9.3.5.4 Converting unstructured data into vectors . 31
5.10 Data Security . 31
5.10.1 Introduction. 31
5.10.2 Artificial Intelligence Technology . 31
5.10.3 Cloud Computing Technology . 32
5.10.4 Firewall Technology . 32
5.10.5 Authentication and Authorization . 32
5.10.6 Data Encryption . 32
5.10.7 Data Masking . 32
5.10.8 Data Backup and Elasticity technology . 32
5.10.9 Data Erasure Technology . 32
6 Example Scenarios to Illustrate Data Mechanisms . 33
6.1 AI-enabled Traffic Classification Use Case . 33
6.1.1 Introduction. 33
6.1.2 Data Acquisition . 33
6.1.3 Data Processing for traffic classification . 33
6.2 Network Fault Root-Cause Analysis and Intelligent Recovery Use Case . 34
6.2.1 Introduction. 34
6.2.2 Data Acquisition . 34
6.2.3 Data Processing . 34
6.3 Intelligent Service Experience Evaluation Use Case . 35
6.3.1 Introduction. 35
6.3.2 Data Acquisition . 35
7 Example requirements . 36
7.1 Data format and interface . 36
7.2 Data Security . 36
7.2.1 Data Security introduction . 36
7.2.2 Data Collection Security example requirements . 36
7.2.3 Data Transmission Security example requirements . 37
7.2.4 Data Storage Security example requirements . 37
7.2.5 Data Processing Security example requirements . 37
7.2.5.1 Data Processing introduction . 37
7.2.5.2 Data Desensitization example requirements . 37
7.2.5.3 Data Analysis Security example requirements . 37
7.2.5.4 Data Import and Export security example requirements . 38
7.2.6 Data Exchange Security example requirements . 38
7.2.7 Data Destruction Security example requirements . 38
8 Recommendations . 38
Annex A: Change History . 40
History . 41

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Intellectual Property Rights
Essential patents
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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.
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.
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• Clause 7 presents example requirements for data format, interface and data security.
• Clause 8 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 revises ETSI GR ENI 009 [i.30] (V1.1.1). The realization of intelligent network depends on the
use of mechanisms related with: processing of the big data, AI algorithms and computing resources. Therefore, effective
data management and operation is extremely important.
The present document is purposed to enhance the ETSI GR ENI 009 [i.30] (V1.1.1) on data operation requirements and
mechanisms to better serve ENI system, especially within the following technical areas:
1) data format among the Functional Block of ENI system and towards the external world (internal Functional
Blocks, Knowledge Representation);
2) data conversion and possibility to translate AI data model to be adapted to/from external system (external
trained model imported into ENI);
3) consistency of data format and interface to accelerate the Autonomous Network (AN) evolution process; and
4) ensure that customer privacy is not disclosed in the entire lifecycle of data collection, processing and
utilization (Federated Learning).
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 (V3.2.1): "Experiential Networked Intelligence (ENI); ENI use cases".
[i.2] ETSI GR ENI 004 (V3.1.1): "Experiential Networked Intelligence (ENI); Terminology".
[i.3] ETSI GS ENI 005 (V3.1.1): "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)".
[i.7] IETF RFC 5357: "A Two-Way Active Measurement Protocol (TWAMP)".
[i.8] IETF RFC 9197: "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)".
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[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-12: "Enhanced Alternate Marking
Method".
[i.18] IETF I.D.draft-song-ippm-postcard-based-telemetry-15: "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).
[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.
[i.25] The Atlan Data wiki definition of unstructured data.
[i.26] IETF RFC 4560: "Definitions of Managed Objects for Remote Ping, Traceroute, and Lookup
Operations".
[i.27] Prometheus open source.
[i.28] NoSQL.
[i.29] Data model.
[i.30] ETSI GR ENI 009 (V1.1.1): "Experiential Networked Intelligence (ENI); Definition of data
processing mechanisms".
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
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
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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.
ENI AI data model: AI data model refers to the data involved in AI modeling process
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.
Principal Component Analysis (PCA): data dimensionality reduction algorithm
NOTE: The central idea of principal PCA is to reduce the dimensionality of a data set consisting of a large
number of interrelated variables, while retaining as much as possible of the variation present in the data
set.
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
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
PCA Principal Component Analysis
QoS Quality of Service
SDK Software Development Kit
SDN Software-Defined Networking
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
YAML Yet Another 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 equipment's 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, a way of efficiently processing and managing continuous
data is needed.
NOTE: More consideration on "historical data" will be described in a future version in a later release.
5 Data Mechanism
5.1 Introduction
5.1.1 Data Mechanism Overview
This clause defines components in a high-level overview for data acquisition and processing. Furthermore, this clause
classifies different types of data in terms of their data sources, as well as describes data processing mechanisms, in order
to support AI enabled network OAM and service management.
The Data Mechanism supports different data acquisition and processing mechanisms for data from different sources and
for use by different network applications.
As shown in figure 5-1, the data mechanism overview is able to be partitioned into the following components.
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Data
Data serving
Data Sharing
Data Processing
Management
Data Data Data Feature
...
cleaning correlation enhancement extration
Metadata
Sharing Model
management
Data training
Data Storage
Data lake Data Warehouse Data Mart .
Data security
management
Data Collection
Sharing AI
model inference
Network Resource Fault Streaming
...
telemetry telemetry telemetry telemetry
Data quality
management
Data Source
Network- Application- Business
External data
related data aware data support data

NOTE: The content in grey box will be described in a future version in a later release.

Figure 5-1: Data Mechanism Overview
The main components above are thoroughly described and explained in the next clauses. However, before doing that,
some information will be provided on the data contents characteristics, i.e. on the types of data that are able to be used
to classify data as well as on the parameters that encompass each type and the scenarios where they could be found.
Telemetry is a service/application related to the collection of measurements, statistics, or other related data at
pre-determined points, and the subsequent and automatic transmission of those data to appropriate devices. It will be
used throughout this clause as an example of data source in order to provide some practical application to the
descriptions presented in the main text.
5.2 Data Characteristics
5.2.1 Configuration Data
Configuration data are used to identify the context in which measurements are made. Table 5-1 lists some examples of
configuration data that are required to be made per-user, per-service telemetry measurements, see clause 5.4.1.
Table 5-1: Exemplary Configuration Data Characteristics
Configuration Data Brief Description Source Scenario
Network device attribute Device ID, location, device Network Management Network device alarm root
information model/version Systems --> OSS cause analysis
Network device Device IP, port, Vlan ID, IP Network Management Intelligent traffic steering
configuration information Route Protocol Systems
Customer information IMEI, IMSI, Terminal type, user Network Management Content Recommendation
name, user level (e.g. VIP Systems --> BSS
user), register time,
subscription service information

5.2.2 Sequential Data
Sequential data are a series of data recorded in time order. Table 5-2 shows some examples of sequential data.
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Table 5-2: Exemplary Temporal Data Characteristics
Sequential Data Brief Description Source Scenario
Fault data Alarm, log Network Management Systems Network device alarm
root cause analysis
Performance data CPU, memory, and I/O Network infrastructure --> servers KPI anomaly analysis
usage memory
Network traffic data Throughput, rate, delay Network infrastructure --> switches, Traffic prediction
routers
External environment data Temperature, humidity External sources --> sensors Device equipment
energy saving
5.2.3 Data Representation
Data is able to be classified into structured, semi-structured, and unstructured data formats according to whether the
data can be expressed in a uniform structure.
Structured data is information organized in a predetermined way (a fixed format, data model or schema) within a record
or a file [i.24]. Structured data enables all elements to be individually addressable, and conform to a data model.
Table 5-3 shows some examples of structured data in the network.
Table 5-3: Exemplary Structured Data Characteristics
Structured Data Brief Description Source Scenario
Relational Data Data structured that adheres to a pre-defined data SQL database Customer information
model
Semi-structured data is 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.
Table 5-4: Exemplary Semi-structured Data Characteristics
Semi-Structured Data Brief Description Source Scenario
XML Data Data that has some organizational XML Data Store Some types of Network Data
properties
Unstructured data is information that does not have a pre-defined data model, and does not contain properties that
provide any organization or structure to its elements. It will be pre-processed in order to find information by
domain-specific applications [i.25]. Table 5-5 shows some examples of unstructured data in the network.
Table 5-5: Exemplary Unstructured Data Characteristics
Unstructured Data Brief Description Source Scenario
Word®, PDF®, or Text Data that does not have a pre-defined BSS Content
Documents, Media Files data model (e.g. streaming media)

5.2.4 Data Exchange Formats
When using interfaces for data exchange between functional blocks, according to what is defined in ETSI
GR ENI 004 [i.2] three data formats are usually used: JSON, XML and YAML:
• JSON: It is a lightweight text data exchange format, which is syntactically the same as the code for creating
JavaScript objects and consists of key&value.
• XML: It is an extensible markup language, a subset of the standard universal markup language, and a markup
language used to mark electronic files to make them structured.
• YAML: It is an intuitive data serialization format that can be recognized by the computer.
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5.2.5 Data Model from FBs in ENI System
5.2.5.1 Data model types
A Data Model is an abstraction used to represent real world entities, the relationship between these entities and the
operations that can be performed on the data. In ENI System, the data model determines how a FB encodes its data and
then can be seen and understood by other FBs.
There are three basic data models in the process of data development, they are Hierarchical Model, Network model and
Relational Model. The three models are named after their data structures. Hierarchical Model and Network model use
structured data. Relational models are unstructured data structures. The basic structure of the hierarchical model is a tree
structure; the basic structure of the network model is an undirected graph without any restrictions. The relational model
is an unformatted structure, and a single two-dimensional table structure is used to represent the relationship between
entities and entities. The relational model is a commonly used data model in the current database.
Hierarchical Model: Organize the data into a one-to-many relationship structure, and use a tree structure to represent
entities and the connections between entities.
Network Model: Using connection instructions or pointers to determine the mesh connection relationship between data
is a many-to-many type of data organization.
Relational Model: Organize data in the form of record groups or data tables, so as to use the relationship between
various entities and attributes for storage a
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