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Presented by: © ETSI 2018 17-November-2018 ETSI ISG ENI** How do big data and artificial intelligence help in QoE assurance Chairman: Dr. Raymond Forbes (Huawei Technologies) Vice-Chairman: Mrs. Haining Wang (China Telecom) Vice-Chairman: Mr. Fred Feisullin “Fred” (Verizon) Secretary: Dr. Yue Wang (Samsung) Technical Officer: Mrs. Korycinska Sylwia (ETSI) Technical Manager: Dr. Shucheng Liu “Will” (Huawei Technologies) **Experiential Networked Intelligence

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Page 1: d^/ /^' E/ , } Á } ] P v ] ( ] ] o ] v o o ] P v Z o ] v Y ... · d Z d^/ Á Á Á X ] X } P Z } Á ] v } Á Z } Á v o Ç µ o ] Z E/ o ] À o W í X d^/ 'Z E/ ì ì í s í X í

Presented by:

© ETSI 2018

17-November-2018

ETSI ISG ENI**

How do big data and artificial

intelligence help in QoE assurance

Chairman: Dr. Raymond Forbes (Huawei Technologies)Vice-Chairman: Mrs. Haining Wang (China Telecom)Vice-Chairman: Mr. Fred Feisullin “Fred” (Verizon)Secretary: Dr. Yue Wang (Samsung)Technical Officer: Mrs. Korycinska Sylwia (ETSI)Technical Manager: Dr. Shucheng Liu “Will” (Huawei Technologies)

**Experiential Networked Intelligence

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© ETSI 2018 2

Outline

ETSI ISG ENI progress - Chairman

• Vision & background

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

• Discussion about the analytics of big data to detect anomalies: improve the service QoE

• Other related SDOs and industry consortia

• Network intelligence activities in 2016, 2017 & 2018

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© ETSI 2018 3

ENI Workplan

Experiential Networked Intelligence (ENI) Work Items and Rapporteurs:

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

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

• ENI 003 (WI DGR/ENI-003) Context Aware Policy Modelling – Strassner, John (Huawei)

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

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

• ENI 006 (WI DGS/ENI-006) PoC Framework – Pesando, Luca (TIM) / Mostafa, Essa (Vodafone)

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

• New work item under discussion: analytics for big data to detect anomalies: improve the service QoE – China Telecom, etc..

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© ETSI 2018 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

QoE assurance in service delivery

Increased profit

5G/IoT automation

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

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© ETSI 2018 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

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© ETSI 2018 6

ENI Goals, Members and Participants

The ISG ENI Leadership team

Role Company

Chairman Huawei (Dr. Raymond Forbes)

Vice Chairman China Telecom (Mrs. Haining Wang)

Second Vice Chairman Verizon (Mr. Farid Feisullin “Fred”)

Technical Officer ETSI (Mrs. Sylwia Korycinska)

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

Secretary Samsung (Dr. Yue Wang)

ENI ISG PoC Review Team

Raymond Forbes (Huawei) ENI ChairmanSylwia Korycinska (ETSI Technical Officer)Michele Carignani (ETSI CTI) Haining Wang (China Telecom) & ENI Vice Chairman

Luca Pesando (TIM)Mostafa Essa (Vodafone)Antonio Gamelas (Portugal Telecom)

ETSI ISG ENI founded at 17Q1• 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.

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

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

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© ETSI 2018 7

Participants

Members

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

Officials

PoCReview Team

ENI Members and Participants- 41 operators, vendors and research institutes from across Europe, USA and Asia

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© ETSI 2018 8

Objectives

Identify requirements to improve experience

Engage with SDOs and consortia

Publish reference architecture

Enable intelligent service assurance Standardize how the network

experience is measured

Develop the use of big data analytics to detect anomalies:

improving the service QoE

Quote the ETSI ENI white paper: “The ENI architecture will show how these functional entities, including: APIs, and DSLs can be integrated. In particular, the ISG ENI focuses on intelligent service operation and management based on emerging technologies, such as big data analysis, analytics, and artificial intelligence tools, and on automating complex human-dependent decision-making processes.” It is expected that QoE will be improved.

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© ETSI 2018 9

Use Cases

Service Orchestration and Management Context aware VoLTE service experience optimization

Intelligent network slicing managementIntelligent carrier-managed SD-WAN

Network AssuranceNetwork fault identification and prediction

Assurance of service requirements

Infrastructure ManagementPolicy-driven IDC traffic steering

Handling of peak planned occurrences Energy optimization using AI

Network OperationsPolicy-driven IP managed networks

Radio coverage and capacity optimizationIntelligent software rollouts

Policy-based network slicing for IoT securityIntelligent fronthaul management and orchestration

Elastic Resource Management and Orchestration

Application Characteristic based Network Operation

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

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© ETSI 2018 10

Conceptual ENI Architecture

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© ETSI 2018 11

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.

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© ETSI 2018 12

Initial Thoughts on Reference Architecture

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© ETSI 2018 13

Initial Published Deliverables

The ETSI www.etsi.org shop window shows recently published ENI deliverables:

1. ETSI GR ENI 001 V1.1.1 (2018-04) PublishedExperiential Networked Intelligence (ENI); ENI use cases

2. ETSI GS ENI 002 V1.1.1 (2018-04) PublishedExperiential Networked Intelligence (ENI); ENI requirements

3. ETSI GR ENI 003 V1.1.1 (2018-05) PublishedExperiential Networked Intelligence (ENI); Context-Aware Policy Management Gap Analysis

4. ETSI GR ENI 004 V1.1.1 (2018-05) PublishedExperiential Networked Intelligence (ENI); ENI Terminology

5. ETSI GS ENI 006 V1.1.1 (2018-05) PublishedExperiential Networked Intelligence (ENI); ENI Proof of Concept (PoC) Framework

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© ETSI 2018 14

Ecosystem

Standard & Industry

ETSI ENI

CCSA TC 610 - AIAN(AI applied to Network)

ITU-T SG12, MEF, TMF(Policy, Models, Architecture）

IETF, 3GPP(Protocols, Architecture）

BBF, GSMA（Fixed / Mobile industrial

development）

ITU-T SG13 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.

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© ETSI 2018 15

Other related SDOs, Industry Consortia and EU ProjectsOrganization ActivityITU-T Focus Group on Machine Learning for Future Networks including 5G

IETF ANIMA IETF Research group on Autonomic Networking Integrated Model and Approach

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

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

TMF 5G Intelligent Service Operations

Oasis Advanced systems interworking – Open intelligent protocols

Linux Foundation ONAP, Acumos

Bodies with no active interaction or liaisonTelecom Infra Project (TIP) Artificial Intelligence (AI) and Applied Machine Learning (ML) Project Group

IEEE ComSoc Network Intelligence Emerging Technology Initiative (ETI)

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© ETSI 2018 16

Liaison with ITU-T SG12

Expected Liaison with SG12:

ETSI ISG ENI is focusing on developing the requirements, architectures, and maybe functions of an intelligent network.

SG12 is providing QoS/QoE parameters definitions, measurements, models and methodologies,which could be input to ENI and referenced by ENI.

The two groups can usefully and productively cooperate and share synergy.- there is a need to establish liaison- there is a need to be aware of each others activities- there is common membership and the basis for good cooperation- Liaison works best where there is the intention to work together

and the ability to use and contribute to each others recommendations/specifications

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© ETSI 2018 17

Please Contribute

Contact Details:Chair: Dr. Raymond Forbes [email protected]

+44 771 851 1361

Thank you!

Future expected issues:Version 2 on the Use cases & requirements (updates on Version 1)

– New Use Cases– Priority Use Cases demonstrated in PoCs

Development of the system architecture – Possible re-use of existing APIs– Intent policies – AI tools

Discussion about the analytics of big data to detect anomalies– improve the service QoE

Establishment of ENI PoC, Contribution to PoC, assurance of PoC & Validation of the ENI System Architecture

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

ETSI ENI#8 meeting will be held in Telefonica/5tonic in Madrid, Spain, on 3-5 December, 2018.

You are welcome to join us!

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© ETSI 2018 18

Backup material

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Table 1: Classification of Network Intelligence， Example of categorization reportCategorization - level

Name Definition Man-machine interface

Decision-making participation

Data collection and analysis

Degree of intelligence

Environment adaptability

Supported Scenario

Level0 Traditional manual

network

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

logs.

How (command) All-manual Single and shallow awareness (SNMP events and alarms)

Lack of understanding

(manual understanding)

Fixed Single scenario

Level 1: Partially automated

networkautomateddiagnostics

Automated scripts are used in service provisioning, network deployment, and maintenance. Shallow perception of network status and decision making suggestions of machine;

How (command) Provide suggestions for machines or humans

and help decisionmaking.

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 (declarative) 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 (declarative) Most of the machines make decisions

Comprehensive and adaptive sensing (such as data compression and optimization technologies)

Comprehensive knowledgeForecast

Changeable Multiple scenarios and combinations

Level4 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 knowledgeForward forecast

Changeable Multiple scenarios and combinations

Level5 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&combin

ation

Autonomous capability

Continuous improvem

ent

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© ETSI 2018 20

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

ENI has set up a PoC review team

Draft of the GS on Process PoC Framework and empty template.

Procedures:

ISG ENI should 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

New PoC topic

New PoC topic identification

Comments

Comments

Yes

Yes

PoC proposal

No

PoC Report

No

PoC proposal review

PoC projectlifetime

PoC report review

PoC proposal preparation

Accepted?

PoC team PoC review team

ISG 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

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© ETSI 2018 21

Other ETSI internal Technical Bodies (TCs/ISGs)

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 OSG OSM Industry Standardization Group on Open Source MANO

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

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

3GPP SA2 3GPP SA5

Mobile standardization specification global partnership project

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© ETSI 2018 22

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 - 2018

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

ENI & SliceNet workshop, Dec’17


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