Presented by:

© ETSI 2019

11-April-2019

ETSI ISG ENI**Creating an intelligent service

management solutionChairman: Dr. Raymond Forbes (Huawei Technologies)Vice-Chairman: Mrs. Haining Wang (China Telecom)Vice-Chairman: Dr. Luca Pesando (Telecom Italia)Secretary: Dr. Yue Wang (Samsung)Technical Officer: Mrs. Korycinska Sylwia (ETSI)Technical Manager: Dr. Shucheng Liu “Will” (Huawei Technologies)

**Experiential Networked Intelligence

© ETSI 2019 2

Outline

ETSI ISG ENI progress

• Vision & background

• Introduce the status of the ETSI ISG on Experiential Networked Intelligence (ENI)

• Network intelligence activities in 2016, 2017 & 2018

• Other related SDOs and industry consortia

• WI progress

© ETSI 2019 3

ENI Published Deliverables & Workplan

Ongoing ENI Work Items and Rapporteurs:

• ENI 001 (WI RGS/ENI-008) Use Cases (Release 2) – Yue Wang (Samsung)

• ENI 002 (WI RGS/ENI-007) Requirements (Release 2) – Haining Wang (China Telecom)

• ENI 004 (WI RGR/ENI-010) Terminology (Release 2) – Yu Zeng (China Telecom)

• ENI 005 (WI DGS/ENI-005) System Architecture – John Strassner (Huawei)

• ENI 006 (WI RGS/ENI-012) PoC Framework (Release 2) – Bill Wright (Redhat/IBM)

• ENI 007 (WI RGR/ENI-011) Definition of Networked Intelligence Categorization – Luca Pesando (TIM)

Published ENI deliverables:

• ETSI GR ENI 001 V1.1.1 (2018-04) PublishedUse Cases – Yue Wang (Samsung)

• ETSI GS ENI 002 V1.1.1 (2018-04) PublishedRequirements – Haining Wang (China Telecom)

• ETSI GR ENI 003 V1.1.1 (2018-05) PublishedContext-Aware Policy Management Gap Analysis – John Strassner (Huawei)

• ETSI GR ENI 004 V1.1.1 (2018-05) PublishedTerminology – Yu Zeng (China Telecom)

• ETSI GS ENI 006 V1.1.1 (2018-05) PublishedProof of Concept (PoC) Framework - Luca Pesando (TIM)

Accessible via Work Item Monitoring - ENI

© ETSI 2019 4

Business Value

• Network： Traditional SDN & NFV Autonomic Network

Better customer experience

Improved QoE of service

Increased service value

▪ Human decisions

▪ Complex manual

configuration

Network mgmt. and operation evolution

▪ Rapidly changing

network conditions

▪ More services,

more users

Network technology evolution

Enhanced network experience

▪ Network perception and analysis

▪ Data driven policy

▪ AI-based closed-loop control

ENI

Management and operation intelligence

Network intelligence

Improved business efficiency

Reduced OPEX

Increased profit

5G/IoT automation

Better QoE service delivery

Source: ETSI ENI White Paper, http://www.etsi.org/images/files/ETSIWhitePapers/etsi_wp22_ENI_FINAL.pdf

© ETSI 2019 5

Vision

ENI

TraditionalTelecom Hardware

Manual

Operation

& Mgmt

+

AI-based

Operation

& Mgmt

Manual

Operation

& Mgmt

SDN&NFV Telecom Software & Hardware

SDN &

NFV

Step 1 Step 2 Step 3

ETSI ISG NFV ETSI ISG ENI

SDN&NFV Telecom Software & Hardware

© ETSI 2019 6

ENI Goals, Members and Participants

The ISG ENI Leadership team

Role Name (Organization)

Chairman Dr. Raymond Forbes (Huawei)

Vice Chairman Mrs. Haining Wang (China Telecom)

Second Vice Chairman Dr. Luca Pesando (Telecom Italia)

Secretary Dr. Yue Wang (Samsung)

Technical Officer Mrs. Sylwia Korycinska (ETSI)

Technical Manager Dr. Shucheng Liu “Will” (Huawei)

ENI ISG PoC Review Team

Raymond Forbes (Huawei)

Sylwia Korycinska (ETSI Technical Officer)

Michele Carignani (ETSI CTI)

Haining Wang (China Telecom)

Luca Pesando (Telecom Italia)

Mostafa Essa (Vodafone)

Antonio Gamelas (Portugal Telecom)

ETSI ISG ENI founded at 17Q1, Release 1 (2017-2019)

• The ISG ENI focuses on improving the operator experience,

adding closed-loop artificial intelligence mechanisms based

on context-aware, metadata-driven policies to more quickly

recognize and incorporate new and changed knowledge,

and hence, make actionable decisions.

• In particular, ENI will specify a set of use cases, and the

architecture, for a network supervisory assistant system

based on the ‘observe-orient-decide-act’ control loop

model.

• This model can assist decision-making systems, such as

network control and management systems, to adjust

services and resources offered based on changes in user

needs, environmental conditions and business goals.

Extended at 19Q1 into Release 2(2019-2021)

• New Terms of reference included: implementation, PoC,

plug-tests and open-source relationships

Core idea: Network perception analysis, data-driven policy, AI based closed-loop control

Source: https://portal.etsi.org/TBSiteMap/ENI/ListOfENIMembers.aspx

© ETSI 2019 7

Participants

Members

Source: https://portal.etsi.org/TBSiteMap/ENI/ListOfENIMembers.aspxMembers signed the ENI Member agreement and are ETSI membersParticipants signed the ENI Participant agreement but are not ETSI members

ENI Members and Participants- operators, vendors and research institutes from across Europe, USA and Asia

PoC Review Team

Officials

© ETSI 2019 8

Objectives

Identify requirements to improve experience

Engage with SDOs and consortia

Standardize how the network experience is measured

Publish reference architecture

Enable intelligent service assurance

© ETSI 2019 9

Use Cases

Service Orchestration and Management Context aware VoLTE service experience optimization

Intelligent network slicing management

Intelligent carrier-managed SD-WAN

Intelligent caching based on prediction of content popularity

Network AssuranceNetwork fault identification and prediction

Assurance of Service Requirements

Network Fault Root-cause Analysis and Intelligent Recovery

Infrastructure ManagementPolicy-driven IDC traffic steering

Handling of peak planned occurrences

Energy optimization using AI

Source: ETSI RGS/ENI-008 , Experiential Networked Intelligence (ENI); ENI use cases; – Wang, Yue (Samsung)

Network OperationsPolicy-driven IP managed networks

Radio coverage and capacity optimization

Intelligent software rollouts

Policy-based network slicing for IoT security

Intelligent fronthaul management and orchestration

Elastic Resource Management and Orchestration

Application Characteristic based Network Operation

AI enabled network traffic classification

Intelligent time synchronization of network

© ETSI 2019 10

ENI High-Level Functional Architecture

High-Level Functional Architecture Diagram in DGS/ENI-005 (GS ENI 005) v0.0.21

© ETSI 2019 11

Initial Thoughts on Reference Architecture

Functional Architecture with its Input Reference Points Functional Architecture with its Output Reference Points

© ETSI 2019 12

ENI Assisting MANO

• For MANO to take full advantage of ENI, existing interfaces extension or in some cases, new interfaces may be required• Physical Network interaction, e.g. with SDN Controller explicitly depicted through NFVI interaction (OOB possible too)

ETSI MANO as an Existing System

ENI

Proposed Next State,Proposed Policy change, Requested “state” info

Current “State” info

Current VIM info

Current NFVI info

Current VNF info

Policyor

© ETSI 2019 13

ENI Assisting MEF LSO RA

ENI

Current “State” info

Current “State” info

Proposed Next State,Requested “state” info

Policy

Proposed Next State,Requested “state” info

• ENI ISG collaborates with MEF on liaison basis

• ENI insights can improve the consumer experience by improving the inter-operator interaction

• Of potential interest can also be LSO extensions to include ENI ENI interaction and collaboration

© ETSI 2019 14

Data Information Knowledge Wisdom

Automatic

Autonomous

Adaptive

Manual

Control

Evolution

Autonomous: Introduction of artificial intelligence

to realize self-* features, based on a robust knowledge representation. Includes context-aware situation awareness as part of a comprehensive cognition framework, and uses policy-based management to enable adaptive and extensible service offerings that respond to changing business goals, user needs, and environmental constraints.

Adaptive: intelligent analysis,

real-time acquisition of network data, perception of network status, generate optimization strategies to enable closed-loop operation.

Automatic: automation of service

distribution, network deployment and maintenance, through the integration of network management and control. Human controlled.

© ETSI 2019 15

Timetable & Deliverables

*SDNIA: SDN/NFV Industry Alliance,**SliceNet: H2020 & 5G-PPP Project

ENI Deliverables

Name Number Rapporteur CompanyEarlyDraft

StableDraft

Draft forApproval

Current Status (10-Apr-2019)

Use Cases GS ENI 001 Yue Wang Samsung Jul’18 Jul’19 Aug’19 Rel.2 progressing (Rel.1 Published 2018-04-20)

Requirements GS ENI 002 Haining Wang China Telecom Jul’18 Jul’19 Aug’19 Rel.2 progressing (Rel.1 Published 2018-04-17)

Context AwarePolicy Modeling

GR ENI 003 John Strassner Huawei Sept’17 Dec’17 Mar’18 Rel.1 Published

Terminology GR ENI 004 Yu Zeng China Telecom Jul’18 Jul’19 Aug’19 Rel.2 progressing (Rel.1 Published 2018-06-08)

Architecture GS ENI 005 John Strassner Huawei Feb’18 Jul’19 Aug’19 Progressing

PoC Framework GS ENI 006 Bill Wright Redhat/IBM Feb’19 Jun’19 Jul’19 Rel.2 progressing (Rel.1 Published 2018-06-08)

Definition of Networked Intelligence

CategorizationGR ENI 007 Luca Pesando TIM Oct’18 Jul’19 Aug’19 Progressing

© ETSI 2019 16

Categorization of Network Intelligence

Au

ton

om

ou

s capab

ilityC

on

tinu

ou

s imp

rovem

ent

Example of categorization report

Category Name Definition Man-Machine Interface

Decision Making Participation

Decision Making and Analysis

Degree of Intelligence

Environment Adaptability

Supported Scenario

Level 0 Traditional manual network

O&M personnel manually control the network and obtain network alarms and logs

How (comman

d)

All-manual Single and shallow awareness (SNMP events

and alarms)

Lack of understanding

(manual understanding

Fixed Single scenario

Level 1 Partially automated

network

automated diagnostics

Automated scripts are used in service provisioning, network deployment, and

maintenance. Shallow perception of network status and decision making suggestions of

machine

How (comman

d)

Provide suggestions for machines or

humans and help decision making

Local awareness (SNMP events, alarms, KPIs, and

logs)

A small amount of analysis

Little change Few scenarios

Level 2 Automated network

Automation of most service provisioning, network deployment, and maintenance

Comprehensive perception of network status and local machine decision making

HOW (declarativ

e)

The machine provides multiple opinions, and the machine makes a

small decision

Comprehensive awareness (Telemetry basic data)

Powerful analysis Little change Few scenarios

Level 3 Self-optimization

network

Deep awareness of network status and automatic network control, meeting users'

network intentions

HOW (declarativ

e)

Most of the machines make decisions

Comprehensive and adaptive sensing (such as

data compression and optimization technologies)

Comprehensive knowledge

Forecast

Changeable Multiple scenarios and combinations

Level 4 Partial autonomous

network

In a limited environment, people do not need to participate in decision-making and adjust

themselves

WHAT (intent)

Optional decision-making response

(decision comments of the challenger)

Adaptive posture awareness (edge collection +

judgment)

Comprehensive knowledge

Forward forecast

Changeable Multiple scenarios and combinations

Level 5 Autonomous network

In different network environments and network conditions, the network can automatically adapt

to and adjust to meet people's intentions

WHAT (intent)

Machine self-decision Adaptive deterioration optimization (edge closed-loop, including collection,

judgment, and optimization)

Self-evolution and knowledge

reasoning

Any change Any scenario &

combination

© ETSI 2019 17

Work Plan

Feb’17ETSI ISG

ENI created

Apr’19ENI#09

Samsung Warsaw

Dec’16Brainstorm

event

•Operator reqs•ENI concepts

Apr’17Kickoff

meeting ETSI

Sep&Oct’17ENI#03

China TelecomBeijing,

SNDIA joint workshop,

Whitepaper published

ENI#02ETSI

May’17 Dec’17ENI#04,

Samsung London,

SliceNet joint workshop

Release 1: Part2Tasks:• Reference architecture• PoCs demonstrating different scenarios• Updated Terminology, Use Cases, and Requirements

Output:• Group Reports and/or Normative Group Specifications based on phase 1 study

Release 1: Part1Tasks:• Use Cases• Requirements • Gap analysis • Terminology

Output:• Group Reports showing cross- SDO functional architecture, interfaces/APIs, and models or protocols, addressing stated requirements

Jun’18ENI#06

TIM Turin

Dec’18ENI#08

5Tonic/Telefonica Madrid

Sep’18ENI#07

China TelecomBeijing

July ’19ENI#10

Portugal Telecom Aveiro

Sept ’19ENI#11

China Telecom Beijing

Dec’ 19ENI#12ETSI(?)

Release 2: Part1Tasks:• Network Intelligence Categorization• System Architecture

• Data model• Interface/ external Reference points

• Security and Validation specification• Implementation:

• PoCs demonstrations• Plug-tests• Open-source

• Categorization, Terminology, Use Cases, and Requirements

Output:• Normative Group Specifications• Group Reports as required

Mar’18ENI#05

ETSI

Release 1 focus on use case & requirements, started designing architecture and PoCs.The ETSI Director-General advised by the ETSI Board approved in Feb. 2019 that ISG ENI is mandated to work on ENI release 2 for 2019-2021.

Four F2F meetings per year: 19Q2 Hosted by Portugal Telecom in Aveiro, 19Q3 Hosted by China Telecom in Beijing. Online meeting every week https://portal.etsi.org/tb.aspx?tbid=857&SubTB=857#5069-meetings

… 2021

© ETSI 2019 18

PoC Team and ENI Work-Flow proposalusing the process under definition in ETSI

Procedures:

ISG ENI draft and approve a PoC framework

Form a PoC review group to receive and review PoC proposals with formal delegation from ISG

Publish the framework (e.g. ENI has set up a wiki)

PoC teams (the proposers – which may include non-members) shall present an initial proposal and a final report, according to the templates given by ISG for review

PoC Team(s) are independent of the ISG – use the process and template of the ISG – Choose a POC Team Leader and draft the proposal

ENI PoC review team:

PoC project wiki: https://eniwiki.etsi.org/index.php?title=Ongoing_PoCs

New PoC

topicNew PoC topic

identification

Comments

Comments

Yes

Ye

s

PoC

proposal

No

PoC

Report

No

PoC proposal

review

PoC project

lifetime

PoC report

review

PoC proposal

preparation

Accepted?

PoC team PoC review

teamISG ENI

Accepted

?

PoC

start

PoC end

PoC topic list

maintenance

Feedback

PoC project contributions

5

4

6

3

1

8

2PoC topics

list

9

PoC

contributions

handling

7

e.g.

contributions

to the system

Architecture

© ETSI 2019 19

ENI PoC List

https://eniwiki.etsi.org/index.php?title=PoC

Title PoC Team Members Main Contact Proposal Start Time

Current Status (Apr-2019)

PoC #1: Intelligent Network Slice Lifecycle Management

China TelecomHuawei, Intel, CATT, DAHO Networks, China Electric Power Research Institute

Haining Wang ENI(18)0001

04r2Jun-2018 Stage 2 finished

PoC #2: Elastic Network Slice Management

Universidad Carlos III de MadridTelecom Italia S.p.A., CEA-Leti, Samsung R&D Institute UK, Huawei

Marco Gramaglia ENI(18)0001

75r4Nov-2018 Ongoing

PoC #3: Securing against Intruders and other threats through a NFV-enabled Environment (SHIELD)

TelefonicaSpace Hellas, ORION, Demokritos (NCSR)

Diego R. Lopez Antonio Pastor

ENI(18)0002

34r1Jan-2019 Finished

PoC #4: Predictive Fault management of E2E Multi-domain Network Slices

Portugal Telecom/Altice LabsSliceNet Consortium (Eurescom,University of the West

Scotland,Nextworks S.R.L,Ericsson TelecomunicazioniSpA,IBM,Eurecom,Universitat Politècnica de Catalunya ,RedZinc Service Ltd.,OTE – The Hellenic Telecommunications Organisation, SA,OrangeRomania / Orange France,EFACEC,Dell EMC,Creative Systems Engineering,Cork Institute of Technology)

António GamelasRui Calé

ENI(19)0000

55Mar-2019 Ongoing

PoC #5: AI Enabled Network Traffic Classification

China MobileHuawei, Intel, Tsinghua University

Weiyuan Li ENI(19)009_

004r5Proposedin Apr 2019

Proposed

© ETSI 2019 20

ENI PoC project #1: Intelligent Network Slice Lifecycle Management

https://eniwiki.etsi.org/index.php?title=PoC#PoC.231:_Intelligent_Network_Slice_Lifecycle_Management

• For generating new scale up/down and converting the intent to suggested configuration.

• LSTM is used for traffic prediction.

AI-based predictor:

TNSM:

CNSM:

• Provides underlay network control to satisfy the network slice requests.

• FlexE and a FlexE-based optimization algorithm are used for underlay network slice creation and modification.

• Provides core network control to satisfy the network slice requests

✓ PoC Project Goal #1: Demonstrate the use of AI to predict the change of traffic pattern and adjust the configuration of network slice in advance.✓PoC Project Goal #2: Demonstrate the use of intent based interface to translate tenant requirements to network slice configuration and intelligent network slice lifecycle management on demand.

Host/Team Leader Team members

© ETSI 2019 21

ENI PoC project #2: Elastic Network Slice Management

Orchestration Architecture

Hardware and Software Setup

PoC Team

Network Slice Blueprinting

& Onboarding

Elastic Intelligent Features:

Horizontal and Vertical

VNF Scaling

Intelligent Admission

Control

One innovative service

through 2 network slices

Virtual Reality application

One eMBB slice for 360

video

One URLLC slice for

haptic interaction

Main Features

https://eniwiki.etsi.org/index.php?title=PoC#PoC.232:_Elastic_Network_Slice_Management

VR Service

Goals:

Provide enhanced AI-based mechanisms to provide

novel 5G Services

Design, test and validate new interfaces with MANO

© ETSI 2019 22

ENI PoC project #3: Securing against Intruders and other threats through a NFV-enabled Environment (SHIELD)

Status: PoC public demo 23-25th Feb. 2019, and finished.

Host/Team Leader:

Team members:: https://www.shield-h2020.eu/

NetworkInfrastructure

VNFMon.vNSF

Act.

vNSF

Store

developers

vNSF store

Developer API

Client API

Security overview dashboard

vNSFO API Data engine APIQuery API

Service

Provider

Service

ProviderClient

Northbound API

Orchestrator Engine

vNSF Manager Engine

vNSF orchestratorDARE/ENI

Security events

Trust Monitor

Security

Agency

Einf-eni-dat

Eoss-eni-cmd

Ebss-eni-cmd

Eusr-eni-cmd

Eor-eni-cmd

Remediation

engine

data analytics

engine

data acquisition

and storage

https://eniwiki.etsi.org/index.php?title=PoC#PoC.233:_Securing_against_Intruders_and_other_threats_through_a_NFV-enabled_Environment

Goals

•PoC Goal #1: Demonstrate an AI framework able to detect network attacks over NFV network combining several ML algorithms

•PoC Goal #2: Recommend intent-based security policy

•PoC Goal #3: Remote attestation for data collectors.

Gaps identified

•Coordination between AI models / ENI systems.

•Support 3rd party AI models

• Information sharing between ENI systems

•Data collection integrity and security with Trust Monitoring

•Synchronization between intent-based and configuration policies API

Contribution

•New type of use cases related with malware in ENI 001

© ETSI 2019 23

ENI PoC project #4: Predictive Fault management of E2E Multi-domain Network Slices

Network Service Provider (NSP) A Network Service Provider (NSP) B

Digital Service Provider (DSP)

Energy Distributor(Vertical)

RAN

Power LinePPD1 PPD2 PPD3 PPDN

E2E Network Slices (URLLC slice and SCADA slice)

RAN Slice as a ServiceRAN + EPC + Core IP/MPLS Slice as a Service

EPCCore

IP/MPLSRAN

https://slicenet.eu/

• PoC is focused on the DSP functions• NSPs provide Sub-slices• DSP monitors all Sub-slices behaviour• DSP predicts Sub-slice failure• DSP decides best failover sub-slice alternative• DSP triggers Subslice/NSP switching

PoC Project Goal #1: Network Slice Fault Prediction. Demonstrate the use of AI on performance data to be able to accurately predict failure situations on Network Slices and estimate their impact on an E2E multi-domain slice performance.

PoC Project Goal #2: Policy-based Network Slice Management. Evaluate the use of a policy-based structure for slice composition decisions, as well as the mechanisms for policy definition on that same context.

https://eniwiki.etsi.org/index.php?title=PoC#PoC.234:_Predictive_Fault_management_of_E2E_Multi-domain_Network_Slices

© ETSI 2019 24

Ecosystem

Standard & Industry

ETSI ENI

CCSA TC 610 - AIAN(AI applied to Network)

ITU-T, MEF, TMF(Policy, Models, Architecture）

IETF, 3GPP(Protocols, Architecture）

BBF, GSMA（Fixed / Mobile industrial

development）

ML5G

IEEE – ETI( Intelligence Emerging Technology Initiative)

IRTF NMRG

EU H2020 Projects

Open Source

Linux FoundationONAP, Acumos, PNDA

(Data collection and decision, AI )

Research

• Cooperate with mainstream operators, vendors and research institutes in Europe, USA and Asia• Collaborate with other SDOs and industry ad-hocs

• Liaisons exchanged with IETF, BBF, MEF, ITU-T• Liaisons with other ETSI groups: NFV, NGP, MEC, NTECH, OSM, ZSM

• Position ETSI ENI as the home of network intelligence standards• Guide the industry with consensus on evolution of network intelligence• Boarder between different categories are becoming vague.

© ETSI 2019 25

Forum on Network Intelligence, Dec’16, Shenzhen, China• Orange, NTT, CableLabs, China Telecom, China Unicom, China Mobile, NEC, HPE, HKUST etc, 40+ participants joined • Study of the scope of network intelligence and related technologies, collected ideas of operators and academics• Participants discussed and get rough consensus on the ENI ISG Proposal

ENI & SDNIA Joint Forum on Network Intelligence, Sep’17, Beijing, China• China Telecom, China Unicom, China Mobile, Huawei, Nokia, Intel, Samsung, ZTE, H3C, Lenovo, HPE, etc , 140+ participants joined • ENI main players presented the progress of ENI• Operators shared use cases and practices• Manufacturers shared solution ideas• Demo prototype that embodies the ENI concept• Deep cooperation between SDNIA and ENI was discussed, after that SDNIA created new industry group AIAN(AI Applied to

Network)

ENI & H2020-SliceNet Workshop, Dec’17, London, UK• Collaboration on PoC and Common partners identified between ENI and SliceNet• ENI plan to set up a PoC team• SliceNet then could take PoCs as an opportunity to feed requirements, use cases and input for ENI work programme, contributing

one or more PoC proposals

ENI & 5GPPP MoNArch Workshop, June’18, Turin Italy• Collaboration on PoC and Common partners identified between ENI and 5G=MoNArch• Need to Formally submit the PoC Proposal

ENI & CCSA TC 610 ANIA Joint Forum on Network Intelligence, Sep’18, Beijing, China• ENI main players presented the progress of ENI• Operators shared PoC experience and demos with support of Manufacturers• Demo prototype that embodies the ENI concept

Forum on Network Intelligence, Dec’16

Network Intelligence Activities in 2016 - 2019

ENI & SDNIA Joint Forum on Network Intelligence, Sep’17

ENI & SliceNet workshop, Dec’17

© ETSI 2019 26

Other related SDOs, Industry Consortia and EU ProjectsORGANIZATION ACTIVITYITU-T SG13 FG ML5G Focus Group on Machine Learning for Future Networks including 5G

IETF/IRTF ANIMAWG , NMRG

IETF Working Group on Autonomic Networking Integrated Model and Approach IRTF Research Group on Network Management

CCSA TC610 (was SDNIA) AIAN (Artificial Intelligence Applied in Network) industry group

H2020 & 5G-PPP SliceNet, SelfNet, 5G-MoNArch, Shield

BBF SDN&NFV WA CloudCO Project Stream focusing on the CloudCO reference architectural framework

MEF Lifecycle Service Orchestration Committee, Service Committee, Applications Committee

OASIS Advanced systems interworking – Open intelligent protocols

Linux Foundation ONAP, Acumos, PNDA

BODIES WITH NO ACTIVE INTERACTION OR LIAISON, (AT THE MOMENT)Telecom Infra Project (TIP) Artificial Intelligence (AI) and Applied Machine Learning (ML) Project Group

TMForum Telecommunication Management Forum

IEEE ETI NI Network Intelligence Emerging Technology Initiative (ETI)

© ETSI 2019 27

Other ETSI internal Technical Bodies (TCs/ISGs)

ETSI TECHNICAL BODY ACTIVITY

ETSI ISG NFV Industry Standardization Group on Network Functions Virtualization

ETSI ISG MEC Industry Standardization Group on Mobile-access Edge Computing

ETSI ISG ZSM Industry Standardization Group on Zero touch network and Service Management

ETSI OSG OSM Open Source Group on MANO

ETSI TC INT - AFI Technical Committee Core Network and Interoperability Testing - Evolution of Management towards Autonomic Future Internet

ETSI TC CYBER CYBER security centre of expertise

3GPP SA2 3GPP SA5

Mobile standardization specification global partnership project

© ETSI 2019 28

Acknowledge the assistance of

Dr. LIU Shucheng (Will) liushucheng@huawei.com

Chris Cavigioli Chris.Cavigioli@intel.com

Contact Details:Chair: Dr. Raymond Forbes

rayforbes@mail01.huawei.com

+44 771 851 1361

Thank you!

ETSI ENI#10 meeting will be hosted by Portugal Telecom in Aveiro,

Portugal, on Jul 9-11, 2019.

You are welcome to join us!

© ETSI 2019

ENIUse Cases

Dr. Yue Wang (Samsung), Rapporteur, Use Cases

© ETSI 2019 30

Overview of the Use Case Work Item

Identify and describe appropriate use cases

First phase (April 2017 – April 2018)• Scope - use cases and scenarios that are enabled with enhanced experience, through the

use of network intelligence. • Group report (GR) produced: ETSI GR ENI 001 V1.1.1

▪ Details where intelligence can be applied in the fixed and/or mobile network ▪ Gives the baseline on how the studies in ENI will substantially benefit the operators

and other stakeholders.

Second phase (started April 2018)• Gives baseline on how the studies in ENI can be applied as solutions of the identified use

cases in accordance with the ENI Reference Architecture, and will substantially benefit the operators and other stakeholders.

• Group Specification to be produced

© ETSI 2019 31

Use Cases

Service Orchestration and Management Context aware VoLTE service experience optimization

Intelligent network slicing management

Intelligent carrier-managed SD-WAN

Intelligent caching based on prediction of content popularity

Network AssuranceNetwork fault identification and prediction

Assurance of Service Requirements

Network Fault Root-cause Analysis and Intelligent Recovery

Infrastructure ManagementPolicy-driven IDC traffic steering

Handling of peak planned occurrences

Energy optimization using AI

= examples shown in the next slidesSource: ETSI RGS/ENI-008 , Experiential Networked Intelligence (ENI); ENI use cases; – Wang, Yue (Samsung)

Network OperationsPolicy-driven IP managed networks

Radio coverage and capacity optimization

Intelligent software rollouts

Policy-based network slicing for IoT security

Intelligent fronthaul management and orchestration

Elastic Resource Management and Orchestration

Application Characteristic based Network Operation

AI enabled network traffic classification

© ETSI 2019 32

ENI Use Case: Policy-driven IDC traffic steering

➢ Autonomous service volume monitoring➢ Network resource optimization through historical data and prediction in real-time➢ Network traffic via different links is balanced➢ Network resource, such as bandwidth, will be used more efficiently

The role of AI and ENI:

The challenges:

➢ Multiple links between IDCs, allocated by network administrator, e.g., shortest path strategy➢ Link load not sufficiently considered when calculating the traffic path➢ Bandwidth allocated to a tenant is not always fully used all time

IDC 1 IDC 2

Convergence Network 1 Convergence Network 2

Link 1: heavy loaded

Link 2: light loaded

© ETSI 2019 33

ENI Use Case: Energy optimization using AI

➢ Learn and update the usage pattern of the services➢ Autonomously turn the spare servers to idle state➢ Predict the peak hours and wake up the necessary number of servers

The role of AI and ENI:

The challenges:

➢ The servers in a DC take 70% of the total power consumption➢ Servers are deployed and running to meet the requirement of peak hour service –

100% powered-up full time.

Configuration for peak hour

© ETSI 2019 34

ENI Use Case: Policy-based network slicing for IoT security

➢ Signal specific traffic patterns indicating Distributed Denial of Service (DDOS) attack or other type of attacks

➢ Automatically isolate the attacked devices

The role of AI and ENI:

The challenges:

➢ Massive deployment of devices➢ Slices may be dynamically expansible and adaptable in a changing context➢ Methods and technologies of attacks are widely changing

© ETSI 2019 35

RAU

Fronthaul

Cluster

Fro

nth

au

l Orc

he

stra

tio

n

an

d M

an

age

me

nt

Load Estimation

RCC

Data-plane Fronthaul traffic per slice

Functional split, cluster size designation and resource reservation

Low MAC

High PHY

Low PHY

RF

PDCP

RLC

High MAC

ENI Use Case: Intelligent Fronthaul Management and Orchestration

➢ An optimisation framework at the fronthaul➢ Enables flexible and dynamic resource slicing and functional split➢ Real-time optimisation according to, e.g., the changing traffic demand and requirements

The role of AI and ENI:

The challenges:

➢ Multiple factors and changing contexts ➢ Dimensionality of solution space on network resources (power, processing capability, radio

resources, buffering memory, route to be selected across multiple fronthaul nodes)

The role of AI and ENI:

© ETSI 2019 36

ENI Use Case: AI enabled network traffic classification

➢ Current classification methods cannot sufficiently (or fail to) support the growth in the diversity of applications, traffic volume and the proportion of encapsulated traffic

➢ AI enabled Network Traffic classifier transforms bit streams or extracted features into images and models the network traffic classification as the ‘traffic image’ classification.

➢ By collecting data at the transport level (instead of application level) and applying machine learning algorithms, the ENI system is able to achieve accurate classification result with a relatively low overhead.

The challenges:

➢ Network traffic classification can support numerous network closed-loop control in terms of network security, traffic engineering, Quality of Service (QoS)

➢ By providing traffic change or distribution information at network or service level, classification results support policy-making processes and sever as the guideline for Operations support system (OSS) and traffic forecast.

The role of AI and ENI:

© ETSI 2019 37

ENI Use Case: Intelligent Network Slicing Management

AN

SMF UPF …

NSSF

SMF UPF …

AMF

…

PCF

UDM

Network Slice Instance #1

Network Slice Instance #2

Common NFs

3GPP FunctionsNSSF – Network Slice Selection FunctionAMF – Access Management FunctionAN – Access NetworkSMF – Session management FunctionUE – User EquipmentPCF – Policy Control FunctionUDM – User Data ManagementUPF – User Plane FunctionNF – Network Function

Slice Management and Orchestration

things

UE ENI System

➢ Analyze collected data associated to e.g., traffic load, service characteristic, VNF type and constraints, infrastructure capability and resource usage, etc.

➢ Produce a proper context aware policy to indicate to the network slice management entity when, where and how to place or adjust the network slice instance (e.g., reconfiguration, scale-in, scale-out, change the template of the network slice instance)

➢ Dynamically change a given slice resource reservation

The challenges:

➢ Dynamic traffic➢ Allocation of network resources intra/inter slices (VNF scale in/out, up/down)

The role of AI and ENI:

© ETSI 2019 38

ENI Use Case: Assurance of Service Requirements

➢ Predict situations where multiple slices are competing for the same physical resources and employ preventive measures

➢ Predict or detect requirements change and make decisions (e.g., increasing the priority of a given network slice over neighbouring slices) at run time

The role of AI and ENI:

The challenges:

➢ Dedicated slice (e.g., banking, energy provider company)➢ Vital applications of the network to operate core business➢ Very strict requirements (e.g., a personalized SLA)

© ETSI 2019 39

Benefits

• Optimized energy and network resource usage

• Enhanced security

• Simplified operations

• Guaranteed services

• Adaptive to changing context• Enabling autonomous and policy driven operation• Dynamic• Real-time

The role of AI and ENI:

© ETSI 2019 40

Questions or interest in other use cases:

Contact: Yue Wang (Samsung UK)yue2.wang@samsung.com

Thank you!

Contact

© ETSI 2019

ENI Requirements

and Terminology

Haining Wang (China Telecom), Rapporteur, Requirements

Yu Zeng (China Telecom), Rapporteur, Terminology

© ETSI 2019. All rights reserved

© ETSI 2019 42

Overview of ENI requirements Work Item

General information:

Creation Date: 2018-04-20 Type: Group Specification

Work Item Reference:

RGS/ENI-007 Latest version: 2.0.4

Rapporteur: Haining Wang Technical Officer: Sylwia Korycinska

Scope:

This document specifies the requirements related to ENI. This includes the common requirements for both fixed and mobile, fixed specific requirements and mobile specific requirements. The ENI requirements are based on the ENI use case document and identified requirements from other SDOs. These requirements will form the base for the architecture design work. This revision will update the existing requirements where needed and add new functional and non-functional, security and architectural requirements.

© ETSI 2019 43

Categorization of the Requirements

Level 1 Level 2

Service and network requirements

General requirements

Service orchestration and management

Network planning and deployment

Network optimization

Resilience and reliability

Security and privacy

Level 1 Level 2

Functional requirements

Data Collection and Analysis

Policy Management

Data Learning

Interworking with Other Systems

Mode of Operations

Level 1 Level 2

Non-functional requirements

Performance requirements

Operational requirements

Regulatory requirements

Non-functional policy requirements

© ETSI 2019 44

Overview of ENI Work Item - Terminology

General information:

Creation Date: 2018-06-29 Type: Group Report

Work Item Reference:

RGR/ENI-010 Latest version: 2.0.1

Rapporteur: Yu Zeng Technical Officer: Sylwia Korycinska

Scope:

The WI will provide terms and definitions used within the scope of the ISG ENI, in order to achieve a "common language" across all the ISG ENI documentation. This work item will be updated with the general terminology required as the ENI specifications develop.

© ETSI 2019 45

Work Plan and Next Steps

Thank you!

ENI Terminology

Early draft: July 2018

Stable draft: Delayed to align with ENI-005

Draft for approval: Delayed to align with ENI-005

Contact Details:Yu Zeng

(China Telecommunications)

zengyu.bri@chinatelecom.cn

ENI Requirements

Early draft: June 2018

Stable draft: Delayed to align with ENI-005

Draft for approval: Delayed to align with ENI-005

Contact Details:Haining Wang

(China Telecommunications)

wanghn.bri@chinatelecom.cn

© ETSI 2019

ENIIntelligent

Policy Management

Dr. John Strassner, (Huawei), Rapporteur, Context-Aware Policy Management

© ETSI 2019 47

Overview of ENI Work Item – Context Aware Gap-Analysis

General information:

Creation Date: 2017-04-10 Type: Group Report

Work Item Reference:

DGR/ENI-003 Latest version: 1.1.1

Rapporteur: John Strassner Technical Officer: Sylwia Korycinska

Scope:A critical foundation of experiential networked intelligence is context- and situation-awareness. This WI will analyze work done in various SDOs on network policy management in general, and context-aware network policy management specifically, to determine what can be reused and what needs to be developed. The requirements documented in this report will be considered during the architecture design work.

© ETSI 2019 48

Main Contents of the Context-Aware Policy Management WI

Content

Introduction and Approach• Including Introduction to Policy Management, The Policy

Continuum, Types of Policy Paradigms, etc.

Analysis of the MEF PDO ModelIncluding Characteristics, Supported Policy Paradigms, Imperative/Declarative/Intent Policy, etc.

Analysis of the IETF SUPA policy ModelCharacteristics, Supported Policy Paradigms, etc.

Analysis of the TM Forum SID Policy ModelCharacteristics, Supported Policy Paradigms, etc.

© ETSI 2019 49

The Policy Continuum

The number of continua in the Policy Continuum should be determined by the applications using it.

There is no fixed number of continua.

© ETSI 2019 50

Policy Examples: Intent

Intent policies describe the set of computations that need to be

done without describing how to execute them

Policies are written in a restricted natural language

The flow of control is not specified

The order in which operations occur are irrelevant

Each statement in an Intent Policy may require the translation

of one or more of its terms to a form that another managed

functional entity can understand.

Express What should be done,not How to do it.

Intent policy is an ongoing work in MEF, ENI may reuse some of the output

© ETSI 2019 51

Work Plan and Next Steps

The report had been published:GR ENI 003 v1.1.1 “Experiential Networked Intelligence (ENI); Context-Aware Policy Management Gap Analysis”

Contact: Dr. John Strassner (Huawei)strazpdj@gmail.com

Thank you!

© ETSI 2019

Experiential Networked Intelligence

System Architecture

Dr. John Strassner (Huawei, Rapporteur)

© ETSI 2019 53

Overview of ENI Work Item – System Architecture

General information:

Creation Date: 2017-12-12 Type: Group Specification

Work Item Reference:

DGS/ENI-005 Latest version: 0.0.21

Rapporteur: John Strassner Technical Officer: Sylwia Korycinska

Scope:The purpose of this work item is to define the software functional architecture of ENI. This includes:• defining the functions and interactions that satisfy the ENI Requirements• defining an architecture, in terms of functional blocks, that can meet the needs specified by the ENI Use Cases• defining Reference Points that the above functional blocks use for all communication with systems and entities

© ETSI 2019 54

High-Level Functional System Architecture

Optimize

Design Orchestrate

Monitor

Real-time AnalyticsOffline Analytics

AI Guidance,Policy Enforcement using Model-Driven

EngineeringAI Guidance,Policy Enforcement using Model-Driven

Engineering

1. Outer Closed Control Loop for a GivenContext and Long-Term Optimization

2. Inner Closed Control Loop Triggered byContext Change

Closed Loop Control in FOCALE

FOCALE Model-Driven Development (1)

FOCALE was designed for Autonomic Policy Management

FOCALE abstracts the functionality, structure, and behavior of the system being managed using a unique combination of models, ontologies, and logic

Context selects policies, which define behavior; as context changes, policies change

Input state is extracted/inferred from OAMP data and context and compared to desired state

If they are equal, continue monitoring

If not, determine set of actions to return to desired state

Machine learning observes actions taken and dictated by admins to continuously improve knowledge base

© ETSI 2019 57

Initial Thoughts on Reference Architecture

ADD SECTION NAME

© ETSI 2019 58

Knowledge ManagementFunctional Block

The purpose of the Knowledge Management Functional Block is to represent information about both the ENI System as well as the system being managed. Knowledge representation is fundamental to all disciplines of modelling and AI. It also enables machine learning and reasoning – without a formal and consensual representation of knowledge, algorithms cannot be defined that reason (e.g., perform inferencing, correct errors, and derive new knowledge) about the knowledge. Knowledge representation is a substitute for the characteristics and behaviour of the set of entities being modelled; this enables the computer system to plan actions and determine consequences by reasoning using the knowledge representation, as opposed to taking direct action on the set of entities.

There are many examples of knowledge representation formalisms, ranging in complexity from models and ontologies to semantic nets and automated reasoning engines.

© ETSI 2019 59

Context-Aware ManagementFunctional Block

The purpose of the Context-Aware Management Functional Block is to describe the state and environment in which an entity exists or has existed. Context consists of measured and inferred knowledge, and may change over time. For example, a company may have a business rule that prevents any user from accessing the code server unless that user is connected using the company intranet. This business rule is context-dependent, and the system is required to detect the type of connection of a user, and adjust access privileges of that user dynamically.

© ETSI 2019 60

Situation-Awareness ManagementFunctional Block

The purpose of the Situation Awareness Functional Block is for the ENI system to be aware of events and behaviour that are relevant to the environment of the system that it is managing or assisting. This includes the ability to understand how information, events, and recommended commands given by the ENI system will impact the management and operational goals and behavior, both immediately and in the near future. Situation awareness is especially important in environments where the information flow is high, and poor decisions may lead to serious consequences (e.g., violation of SLAs).The working definition of situation awareness for ENI is:

The perception of data and behaviour that pertain to the relevant circumstances and/or conditions of a system or process (“the situation”), the comprehension of the meaning and significance of these data and behaviours, and how processes, actions, and new situations inferred from these data and processes are likely to evolve in the near future to enable more accurate and fruitful decision-making.

© ETSI 2019 61

Policy-based ManagementFunctional Block

The purpose of the Policy Management Functional Block is to provide decisions to ensure that the system goals and objectives are met. Formally, the definition of policy is:

Policy is a set of rules that is used to manage and control the changing and/or maintaining of the state of one or more managed objects.

There are three different types of policies that are defined for an ENI system:

• Imperative policy: a type of policy that uses statements to explicitly change the state of a set of targeted objects. Hence, the order of statements that make up the policy is explicitly defined.In this document, Imperative Policy will refer to policies that are made up of Events, Conditions, and Actions.

• Declarative policy: a type of policy that uses statements to express the goals of the policy, but not how to accomplish those goals. Hence, state is not explicitly manipulated, and the order of statements that make up the policy is irrelevant.In this document, Declarative Policy will refer to policies that execute as theories of a formal logic.

• Intent policy: a type of policy that uses statements to express the goals of the policy, but not how to accomplish those goals. Each statement in an Intent Policy may require the translation of one or more of its terms to a form that another managed functional entity can understand.In this document, Intent Policy will refer to policies that do not execute as theories of a formal logic. They typically are expressed in a restricted natural language, and require a mapping to a form understandable by other managed functional entities.

An ENI system MAY use any combination of imperative, declarative, and intent policy to express recommendations and commands to be issued to the system that it is assisting.

© ETSI 2019 63

Cognition ManagementFunctional Block

Cognition, as defined in the Oxford English Dictionary, is “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses”.The purpose of the Cognition Framework Functional Block is to enable the ENI system to understand ingested data and information, as well as the context that defines how those data were produced; once that understanding is achieved, the cognition framework functional block then provides the following functions:

• change existing knowledge and/or add new knowledge corresponding to those data and information• perform inferences about the ingested information and data to generate new knowledge• use raw data, inferences, and/or historical data to understand what is happening in a particular context, why

the data were generated, and which entities could be affected• determine if any new actions should be taken to ensure that the goals and objectives of the system will be met.

A cognition framework uses existing knowledge and generates new knowledge.

A cognition framework uses multiple diverse processes and technologies, including linguistics, computer science, AI, formal logic, neuroscience, psychology, and philosophy, along with others, to analyse existing knowledge and synthesise new knowledge

© ETSI 2019

ENIPoC

Framework

Bill Wright (Redhat/IBM)

© ETSI 2019. All rights reserved

© ETSI 2019 65

PoC Team and ENI Work-Flow proposalusing the process under definition in ETSI

Procedures:

ISG ENI draft and approve a PoC framework

Form a PoC review group to receive and review PoC proposals with formal delegation from ISG

Publish the framework (e.g. ENI has set up a wiki)

PoC teams (the proposers – which may include non-members) shall present an initial proposal and a final report, according to the templates given by ISG for review

PoC Team(s) are independent of the ISG – use the process and template of the ISG – Choose a POC Team Leader and draft the proposal

ENI PoC review team:

PoC project wiki: https://eniwiki.etsi.org/index.php?title=Ongoing_PoCs

New PoC

topicNew PoC topic

identification

Comments

Comments

Yes

Ye

s

PoC

proposal

No

PoC

Report

No

PoC proposal

review

PoC project

lifetime

PoC report

review

PoC proposal

preparation

Accepted?

PoC team PoC review

teamISG ENI

Accepted

?

PoC

start

PoC end

PoC topic list

maintenance

Feedback

PoC project contributions

5

4

6

3

1

8

2PoC topics

list

9

PoC

contributions

handling

7

e.g.

contributions

to the system

Architecture

© ETSI 2019 66

ENI PoC List

https://eniwiki.etsi.org/index.php?title=PoC

Title PoC Team Members Main Contact Proposal Start Time

Current Status (Apr-2019)

PoC #1: Intelligent Network Slice Lifecycle Management

China TelecomHuawei, Intel, CATT, DAHO Networks, China Electric Power Research Institute

Haining Wang ENI(18)0001

04r2Jun-2018 Stage 2 finished

PoC #2: Elastic Network Slice Management

Universidad Carlos III de MadridTelecom Italia S.p.A., CEA-Leti, Samsung R&D Institute UK, Huawei

Marco Gramaglia ENI(18)0001

75r4Nov-2018 Ongoing

PoC #3: Securing against Intruders and other threats through a NFV-enabled Environment (SHIELD)

TelefonicaSpace Hellas, ORION, Demokritos (NCSR)

Diego R. Lopez Antonio Pastor

ENI(18)0002

34r1Jan-2019 Finished

PoC #4: Predictive Fault management of E2E Multi-domain Network Slices

Portugal Telecom/Altice LabsSliceNet Consortium (Eurescom,University of the West

Scotland,Nextworks S.R.L,Ericsson TelecomunicazioniSpA,IBM,Eurecom,Universitat Politècnica de Catalunya ,RedZinc Service Ltd.,OTE – The Hellenic Telecommunications Organisation, SA,OrangeRomania / Orange France,EFACEC,Dell EMC,Creative Systems Engineering,Cork Institute of Technology)

António GamelasRui Calé

ENI(19)0000

55Mar-2019 Ongoing

PoC #5: AI Enabled Network Traffic Classification

China MobileHuawei, Intel, Tsinghua University

Weiyuan Li ENI(19)009_

004r5Proposedin Apr 2019

Proposed

© ETSI 2019 67

ENI PoC project #1: Intelligent Network Slice Lifecycle Management

https://eniwiki.etsi.org/index.php?title=PoC#PoC.231:_Intelligent_Network_Slice_Lifecycle_Management

• For generating new scale up/down and converting the intent to suggested configuration.

• LSTM is used for traffic prediction.

AI-based predictor:

TNSM:

CNSM:

• Provides underlay network control to satisfy the network slice requests.

• FlexE and a FlexE-based optimization algorithm are used for underlay network slice creation and modification.

• Provides core network control to satisfy the network slice requests

✓ PoC Project Goal #1: Demonstrate the use of AI to predict the change of traffic pattern and adjust the configuration of network slice in advance.✓PoC Project Goal #2: Demonstrate the use of intent based interface to translate tenant requirements to network slice configuration and intelligent network slice lifecycle management on demand.

Host/Team Leader Team members

© ETSI 2019 68

ENI PoC project #2: Elastic Network Slice Management

Orchestration Architecture

Hardware and Software Setup

PoC Team

Network Slice Blueprinting

& Onboarding

Elastic Intelligent Features:

Horizontal and Vertical

VNF Scaling

Intelligent Admission

Control

One innovative service

through 2 network slices

Virtual Reality application

One eMBB slice for 360

video

One URLLC slice for

haptic interaction

Main Features

https://eniwiki.etsi.org/index.php?title=PoC#PoC.232:_Elastic_Network_Slice_Management

VR Service

Goals:

Provide enhanced AI-based mechanisms to provide

novel 5G Services

Design, test and validate new interfaces with MANO

© ETSI 2019 69

ENI PoC project #3: Securing against Intruders and other threats through a NFV-enabled Environment (SHIELD)

Status: PoC public demo 23-25th Feb. 2019, and finished.

Host/Team Leader:

Team members:: https://www.shield-h2020.eu/

NetworkInfrastructure

VNFMon.vNSF

Act.

vNSF

Store

developers

vNSF store

Developer API

Client API

Security overview dashboard

vNSFO API Data engine APIQuery API

Service

Provider

Service

ProviderClient

Northbound API

Orchestrator Engine

vNSF Manager Engine

vNSF orchestratorDARE/ENI

Security events

Trust Monitor

Security

Agency

Einf-eni-dat

Eoss-eni-cmd

Ebss-eni-cmd

Eusr-eni-cmd

Eor-eni-cmd

Remediation

engine

data analytics

engine

data acquisition

and storage

https://eniwiki.etsi.org/index.php?title=PoC#PoC.233:_Securing_against_Intruders_and_other_threats_through_a_NFV-enabled_Environment

Goals

•PoC Goal #1: Demonstrate an AI framework able to detect network attacks over NFV network combining several ML algorithms

•PoC Goal #2: Recommend intent-based security policy

•PoC Goal #3: Remote attestation for data collectors.

Gaps identified

•Coordination between AI models / ENI systems.

•Support 3rd party AI models

• Information sharing between ENI systems

•Data collection integrity and security with Trust Monitoring

•Synchronization between intent-based and configuration policies API

Contribution

•New type of use cases related with malware in ENI 001

© ETSI 2019 70

ENI PoC project #4: Predictive Fault management of E2E Multi-domain Network Slices

Network Service Provider (NSP) A Network Service Provider (NSP) B

Digital Service Provider (DSP)

Energy Distributor(Vertical)

RAN

Power LinePPD1 PPD2 PPD3 PPDN

E2E Network Slices (URLLC slice and SCADA slice)

RAN Slice as a ServiceRAN + EPC + Core IP/MPLS Slice as a Service

EPCCore

IP/MPLSRAN

https://slicenet.eu/

• PoC is focused on the DSP functions• NSPs provide Sub-slices• DSP monitors all Sub-slices behaviour• DSP predicts Sub-slice failure• DSP decides best failover sub-slice alternative• DSP triggers Subslice/NSP switching

PoC Project Goal #1: Network Slice Fault Prediction. Demonstrate the use of AI on performance data to be able to accurately predict failure situations on Network Slices and estimate their impact on an E2E multi-domain slice performance.

PoC Project Goal #2: Policy-based Network Slice Management. Evaluate the use of a policy-based structure for slice composition decisions, as well as the mechanisms for policy definition on that same context.

https://eniwiki.etsi.org/index.php?title=PoC#PoC.234:_Predictive_Fault_management_of_E2E_Multi-domain_Network_Slices

© ETSI 2019

Definition of

Networked

Intelligence

Categorization

Luca Pesando (TIM), Rapporteur

© ETSI 2019 72

ENI 007 Overview of ENI Work Item – Definition of Network Networked Intelligence Categorization

General information:

Creation Date: 2018-9-19 Type: Group Report (GR)

Work Item Reference:

DGR/ENI-0011 Latest version: V0.0.8

Rapporteur: Luca Pesando Technical Officer: Sylwia Korycinska

Scope:This Work Item will address the aspects of gradual implementation of networked intelligence and specify a categorization framework for systems based on several different levels (e.g. 5, ranging from fully manual to fully autonomic systems as written in ENI whitepaper). This categorization framework will be based on the amount of network engineer intervention and attentiveness required and the expected interfaces with the assisted system. The details of each level will be defined according to scenarios supported and techniques needed.

© ETSI 2019 73

Categorization of Network Intelligence

Au

ton

om

ou

s capab

ilityC

on

tinu

ou

s imp

rovem

ent

Example of categorization report

Category Name Definition Man-Machine Interface

Decision Making Participation

Decision Making and Analysis

Degree of Intelligence

Environment Adaptability

Supported Scenario

Level 0 Traditional manual network

O&M personnel manually control the network and obtain network alarms and logs

How (comman

d)

All-manual Single and shallow awareness (SNMP events

and alarms)

Lack of understanding

(manual understanding

Fixed Single scenario

Level 1 Partially automated

network

automated diagnostics

Automated scripts are used in service provisioning, network deployment, and

maintenance. Shallow perception of network status and decision making suggestions of

machine

How (comman

d)

Provide suggestions for machines or

humans and help decision making

Local awareness (SNMP events, alarms, KPIs, and

logs)

A small amount of analysis

Little change Few scenarios

Level 2 Automated network

Automation of most service provisioning, network deployment, and maintenance

Comprehensive perception of network status and local machine decision making

HOW (declarativ

e)

The machine provides multiple opinions, and the machine makes a

small decision

Comprehensive awareness (Telemetry basic data)

Powerful analysis Little change Few scenarios

Level 3 Self-optimization

network

Deep awareness of network status and automatic network control, meeting users'

network intentions

HOW (declarativ

e)

Most of the machines make decisions

Comprehensive and adaptive sensing (such as

data compression and optimization technologies)

Comprehensive knowledge

Forecast

Changeable Multiple scenarios and combinations

Level 4 Partial autonomous

network

In a limited environment, people do not need to participate in decision-making and adjust

themselves

WHAT (intent)

Optional decision-making response

(decision comments of the challenger)

Adaptive posture awareness (edge collection +

judgment)

Comprehensive knowledge

Forward forecast

Changeable Multiple scenarios and combinations

Level 5 Autonomous network

In different network environments and network conditions, the network can automatically adapt

to and adjust to meet people's intentions

WHAT (intent)

Machine self-decision Adaptive deterioration optimization (edge closed-loop, including collection,

judgment, and optimization)

Self-evolution and knowledge

reasoning

Any change Any scenario &

combination

© ETSI 2019 74

Please Contribute

Contact Details:Chair: Dr. Raymond Forbes raymond.forbes@huawei.com

+44 771 851 1361

Thank you!

Future expected issues:Release 2 on the Categorization, Use cases & requirements

– New Use Cases– Priority Use Cases demonstrated in PoCs

Development of the system architecture – data models and APIs– Interface definitions and External reference points

Establishment of Implementations– ENI PoC Framework, Contribution to PoC, – assurance of PoC, Plug-tests, Open Source & – Validation of the ENI System Architecture

Standardize how the network experience is measuredWelcome new members: especially to be active in discussions

ETSI ENI#10 meeting will be hosted by Portugal Telecom in Aveiro,

Portugal, on Jul 9-11, 2019.

You are welcome to join us!