When AI meets Network- New Business with AI, New Era of Standards and Industry

网络研究部 刘树成 博士，王雅莉 博士Network Research Dept.， Dr. LIU Shucheng（Will），Dr. WANG Yali

Outline

New Business with AI: Toward Intent-Driven Network &

Autonomous Driving Network

New Era of Standards: Accelerating Intent-Driven Network &

Autonomous Driving Network

Reduces the probability of human errors.

Automates complex networks.

Accelerates service provisioning.

Makes networks understand

service intentions

Predicts and quickly rectifies faults.

Enables networks to optimize

themselves.

Improves the efficiency of detecting threats.

Enables networks to predict threats.

“AI+Connectivity” Enables Networks to Achieve New Business Goals of Enterprises

Simplicity

Automation brings

simplicity to users

Intelligence

Enables Networks

understand business intent

From Autonomous Driving Vehicle to Network

• Edge intelligence layer: UE MR/network performance/hardware status/topology data and fault data detection

• Local intelligence layer: Intent & Automation engine, and Analytics & Intelligence engine

• Cloud intelligence layer: AI training platform, full lifecycle app, network-wide data lake and expert labeled date

Achieving Autonomous Driving Network by Scenarios

Operation and optimization

Planning and design

Service provisioning

Deployment

Scenarios

• Survey

• Site design

• RF design

• Site verification

• Commissioning and integration

• Base station installation

• Fault management

• Big event assurance

• Performance optimization

• New service provisioning:

coverage-based/experience-based

provisioning

• Service-based network slicing

• L0: assisted monitoring capabilities, dynamic tasks have to be executed manually.

• L1: the system executes a certain sub-task based on existing rules to increase execution efficiency.

• L2: closed-loop O&M for certain units under certain external environments, lower the bar for

personnel experience and skills.

• L3: sense real-time environmental changes, optimize and adjust itself to the external environment to

enable intent-based closed-loop management.

• L4: predictive or active closed-loop management of service and customer experience-driven

networks, resolve network faults prior to customer complaints, reduce service outages and customer

complaints, and improve customer satisfaction.

• L5: closed-loop automation capabilities across multiple services, multiple domains, and the entire

lifecycle, achieving autonomous driving networks.

An example of Network Intelligence Categorization

Huawei Intent-Driven Network Enable Business Intelligent

Intelligence

Intelligent insights into

business intents

Simplicity

Ultra-simplified

network architecture

Industry-oriented

open platform

OpenNew service

provisioning Delay

Network O&M OPEX

Self-diagnosis and

automatic problem

solving rate

Unknown threat

detection rate

38%

80%

99%

99%

Context

Data-driven InsightsIntent-based API

Real-time Telemetry Configure NETCONF/YANG

Intent Engine

Automation Engine

Intelligent Engine

Analytics Engine

ETH PON Wi-Fi Microwave ZigBee Bluetooth 11ax/ac/n… Underlay

Network

Overlay

Network

Network

Service

VXLAN VPNs

Virtual Security VNF On-Premise Intelligence

SDN

Business Logic/Policy

Outline

New Business with AI: Toward Intent-Driven Network &

Autonomous Driving Network

New Era of Standards: Accelerating Intent-Driven Network &

Autonomous Driving Network

Activities in Related Organizations

CCSA: TC1/TC3/TC5/TC6/TC610 started related work in various fields of the network. 2017Q4,TC610-AIAN set up as the network intelligence industry group.

ITUT: 17Q4 sets up FG ML5G in SG13 to focus on machine learning related to 5G networks.

IRTF/IETF: 18Q3 began to discuss intents and intellectualization in IRTF NMRG (Network Management Research Group).

2018Q3 NMRG has held Side Meeting

about AI/ML in Networking and

Workshop about Intent Based

Networking.

Preliminary discussions on topics such

as intent and network intelligence for

IETF to work on are still not clear.

The plan of NMRG is to start with intent

classification, cases, and system

architecture.

Led by the Fraunhofer HHI Research

Center in Germany, the management

team consists of research institutes,

colleges, ICT institutions, and carriers.

There are three groups:

WG1: Use cases, services and requirements

WG2: Data formats & ML technologies

WG3: ML-aware network architecture

Scope including traffic prediction,

resource optimization, user analysis and

profile, and data collection and analysis.

Title of the project that has been started. Responsible company

Case Study of Network Artificial Intelligence Application Scenario

China Mobile / China Telecom / Huawei

Intelligent Network Monitoring and Resource Scheduling Based on Hotspot EventsResearch on Artificial Intelligence Engine for IDC Network

China Telecom

Intelligent network control and traffic optimization China Unicom

Research on SDN/NFV Network Artificial Intelligence Application Architecture

China Telecom / China Mobile / Huawei

Research on the Application of Network Health Analysis and Early Warning

China Unicom / China Telecom / Huawei

Intelligent 5G network Datang

Research on Intelligent Deployment of Network Services (vCPE and vBBRAS)

FiberHome

Massive multiple-input multiple-output (MIMO) China Telecom

Intelligent management and optimization of data centers China Unicom

Since 2017, network intelligence has

become a hot topic in SDN/NFV

industry alliance (TC610).

In Oct 2017, China top operators and

vendors gather together set up the

AIAN group, and won the

enthusiastic participation of

mainstream players in the

communications industry.

Several projects are created about

use case and architecture.

ETSI: 17Q2 network intelligent standard group ENI was founded. In 17Q4, ENI released the industry's first network intelligent white paper.

16Q4 starts to operate and 17Q1 is established to balance the competitors' standard industry actions. The core concepts, phases, and reference architecture of intelligent network intelligence are proposed to provide important inputs for the intelligent network and IDN.

Level 1 Level 2

Use Case

Network Operations

Policy-driven IP managed networks

Radio coverage and capacity optimization

Intelligent software rollouts

Policy-based network slicing for IoT security

Intelligent fronthaul management and orchestration

Service Orchestration and Management

Context aware VoLTE service experience optimization

Intelligent network slicing management

Intelligent carrier-managed SD-WAN

Network AssuranceNetwork fault identification and prediction

Assurance of service requirements

Infrastructure Management

Policy-driven IDC traffic steering

Handling of peak planned occurrences

Energy optimization using AI

Name Rapporteur Company Current Status (FEB-2019)

Use Cases Yue Wang Samsung Rel.2 begun (Rel.1 Published 2018-04)

Requirements Haining Wang China Telecom Rel.2 begun (Rel.1 Published 2018-04)

Context AwarePolicy Modeling

John Strassner Huawei Rel.1 Published

Terminology Yu Zeng China Telecom Rel.2 begun (Rel.1 Published 2018-06)

PoC FrameworkLuca PesandoMostafa Essa

TIMVodafone

Rel.2 begun (Rel.1 Published 2018-06)

Architecture John Strassner Huawei Early draft v0.0.17

Definition of Networked Intelligence Categorization

Luca Pesando TIM Early draft v0.0.7

Leading Core Group: WP and 5 WIs Published are widely referred, 3 PoCs started and 1 proposed, and Intelligence Categorization WIs started

About 50 companies，Including 12 T(VDF/TI/PT/CT/NTT…) UCs in 4 categories 13 sub-cats

Work ItemsProgress： 17Q1 founded， Q4 WP，18Q1 UC/Req etc WIs

PoC – CT PoC -TIM & Samsung

PoC – PTPoC -TLF

PoCReview Team

Officials

Key WIs

Network Intelligence Core Standard Group - ETSI ENI （ Experiential Networked Intelligence ）

ENI 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

ENI PoC

Title PoC Team Members Main Contact Start TimeCurrent Status

(Mar-2019)

Intelligent Network

Slice Lifecycle

Management

China Telecom

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

Haining Wang Jun-2018 Stage 1 finished

Elastic Network Slice

Management

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

Marco GRAMAGLIALuca Pesando

Nov-2018 Started

Securing against Intruders and other threats through a NFV-enabled Environment (SHIELD)

TelefonicaSpace Hellas, ORION, Demokritos (NCSR)

Diego R. Lopez Antonio Pastor

Jan-2019 Started

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,Orange Romania / Orange France,EFACEC,Dell EMC,Creative Systems Engineering,Cork Institute of Technology)

António GamelasRui Calé NA

Discussed,To be Proposed early 2019

ENI PoC review team:

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

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

ENI PoC project #1: Intelligent Network Slice Lifecycle 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

ENI PoC project #2: Elastic Network Slice Management

Time Plan: Proposal submission Dec. 2018, PoC demo target date Feb. 2019

Host/Team Leader:

Team members::

PoC Concept: network security management, Isolation at network layer based on NFV

PoC Goal #1: Demonstrate an AI framework able to detect network attacks over NFV network based on the combination of several Machine Learning algorithms

PoC Goal #2: Present a policy-driven control loop:

ML-based attack detection

Mitigation recipes through an intent-based security policy

Translated & implemented in a NFV environment with security VNFs (vNSF)

PoC Goal #3: Remote attestation technology to avoid device and data collecting corruption or tampering.

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

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

15

Title of the project that has been started Responsible company

Case Study of Network Artificial Intelligence Application Scenario China Mobile/China Telecom/Huawei

Intelligent Network Monitoring and Resource Scheduling Based on Hotspot EventsResearch on Artificial Intelligence Engine for IDC Network

China Telecom

Intelligent network control and traffic optimization China Unicom

Research on the Architecture of SDN/NFV Network Artificial Intelligence Application

China Telecom/China Mobile/Huawei

Research on the Application of Network Health Analysis and Early Warning

China Unicom/China Telecom/Huawei

Intelligent 5G network Datang

Research on Intelligent Deployment of Network Services (vCPE and vBBRAS)

FiberHome

Massive multiple-input multiple-output (MIMO) China Telecom

Intelligent management and optimization of data centers China Unicom

At the 2018 Alliance Conference, Mr. Wei's keynote speech, "The New Phase of Network Architecture Reconstruction -The Network of Artificial Intelligence Enablement" emphasized the importance of this direction and became a hot spot in network intelligence.

The 2018Q2 SDN/NFV Industry Alliance officially released the white paper on the network AI application. The above

figure shows the level-1 directory.

SDN/NFV Industry Alliance and ENI Joint Office Network Intelligence Forum

Since 2017, network intelligence has gradually become a hot topic of the SDN/NFV industry alliance. After AIAN group established, the mainstream players in the communications industry participated in the discussion and created WIs.

In the 2018 alliance summit, network intelligence was hop topic from the main conference site to the branch sessions.

During the Beijing meeting of 17Q3 and ETSI ENI, the SDN/NFV Industry Alliance and ENI jointly

organized a network intelligent forum to attract 140+ participants, laying a foundation for the

establishment of the AIAN team.

17Q4, SDN/NFV Industry Alliance -AIAN Team is officially established.

Released the White Paper for the SDN/NFV Industry Alliance Conference in April 18th, 2016.

18Q2, SDN/NFV The industry alliance is incorporated into CCSA and becomes TC610.

The 18Q3 ENI-TC610 network intelligent forum attracted 100+ participants and demonstrated multiple

demos. The first PoC-intelligent network slice PoC of ENI was officially released at the conference.

Progress of TC610 SDN/NFV Industry Alliance -AIAN (Artificial Intelligence Applied to Network )

• Network reconstruction requires manual intelligence.

• Multiple technologies, such as SDN, NFV, cloud computing, 5G, and IoT

• Diversified user requirements require network flexibility and operability.

• The network becomes more complex, heterogeneous, and dynamic.

• Global focus on artificial intelligence (state-of-art):

• ETSI ISG ENI; 3GPP SA2; ITUT FG-ML5G

• Three phases of network AI development:

• Phase 1: Complete a large number of repetitive and inefficient manual labor and human-machine interaction.

• Phase 2: Provides network awareness and prediction capabilities.

• Phase 3: Align with high-level operation intentions and strategies to greatly improve operation economic benefits.

• 11 telecom application scenarios of two categories and four categories:

• Fault alarm, performance optimization, mode analysis, and deployment management

网络人工智能应用场景

网络资源类应用场景 网络业务类应用场景

网络告

警关联

和故障

定位

网络故障告警应用场景 网络性能优化应用场景

网络流

量优化

与拥塞

防控

热点事件分析预测

网络模式分析应用场景 网络部署管理应用场景

用户移动模式分析

网络服务优化部署

智能化

网络切

片编排

管理

网络健康分析预警

基于人

工智能

的网络

节能

无线网

络覆盖

和容量

优化

CCO

5G

Massive

MIMO

自优化

配置

移动网络负载均衡

• Key technologies:• Machine learning, natural language processing, intent API and

strategy, big data technology, artificial intelligence chip• Follow-up work:

• Promote model algorithm design, technology research, and live network pilot in the industry.

Core Contents of the AIAN White Paper

Set up in October 2017. Focus on industry ecosystem forces, jointly carry out artificial intelligence technologies, standards, and industry research, explore new models and new models of artificial intelligence, promote technology, industry, and application R&D, and carry out pilot demonstration, international cooperation is widely carried out to form a global cooperation platform.

Member Conference of the Artificial Intelligence Industry Development Alliance

AI Industry Development Alliance Council

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Expert Committee

AI Open Source Open Promotion Team

AI Computing Architecture and Chip Promotion Team

AIIA Organizational Structure

Standardization and Promotion Workgroup -Telecom Project Team

Telecom is an industry domain of AIIA coverage. Currently, the telecom project team is the sub-team of the application work team of the R&D department.

The standard is provided by the CCSA.

Interaction with ITU-T FG ML5G

Project team

Team leader: Cheng Qiang China Information Communication Research Institute

Deputy team leader: Dr. Liao Jun (China Unicom) Deng Chao (China Mobile)

No.Grou

pTopic Led By

Person who leads the project

1Network

Application of Artificial Intelligence in 5G Mobility Management Technology

Telecommunications Science and Technology Research Institute Co., Ltd.

Ai Ming

2Network

Research and application of artificial intelligence to improve network efficiency and word-of-mouth

China Unicom Network Research Institute

Wei Guanglin

3Network

Intelligent network optimization and O&M system AIOpsChina Mobile Communications Corporation Design Institute

Wang Xidian

4Network

Research on Dynamic Broadcast of Massive MIMO Based on Artificial Intelligence

ZTE Communication Co., Ltd. Zheng Li Ming

5Network

Network Knowledge Map and Key Technologies of Network Intelligence

China Mobile Zhu Lin

6Network

Hierarchical Research on Intelligent Capability of Mobile Communication Network

China Mobile Cao Xi

7Securi

tyResearch on Network Countermeasures Based on Big Data

Harbin Institute of Technology

Wang Xin

8Servic

e

Research and Application Practice of Artificial Intelligence Capabilities for Telecom Operators' Market and Service Scenarios

China Mobile Ren Zhijie

AIIA China Artificial Intelligence Industry Development Alliance

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+ joined

• ENI main players presented the progress of ENI

• Operators shared use cases and practices while manufacturers shared solution ideas

• Demo prototype that embodies the ENI concept

• Deep cooperation between SDNIA and ENI was discussed, then SDNIA created AIAN(AI Applied to Network) industry group

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 AIAN 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

ENI Forum in SDN World Congress, Oct’18, Hague, Netherlands

• ENI main players presented the progress of ENI, including introduction, use cases, PoC and architecture

Presentations was made in IETF / IRTF / BBF / ITUT / SDN Congress / ONS/ TMF / MEF / CCSA …

Forum on Network Intelligence, Dec’16

ENI & SDNIA Joint Forum on Network

Intelligence, Sep’17

ENI & SliceNet workshop, Dec’17

Network Intelligence Activities in 2016 - 2018

EcosystemStandard & Industry

ETSI ENI(Concept, UC/Req, Top-level Arch, Categorization, PoC, etc.)

CCSA TC610 – AIAN and AIIA CT Group(Industrial development in China)

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

IETF, 3GPP(Protocols, Architecture）

BBF, GSMA（Fixed / Mobile Industrial Development）

ITUT ML5G

IEEE ETI( Intelligence Emerging Technology Initiative)

IRTF NMRG

EU H2020 & 5G-PPP Projects

Open Source

Linux FoundationONAP, Acumos, PNDA

(Data collection and decision, AI )

Research

• Expand contributions on existing WIs and PoCs• ETSI ENI established contact with other SDOs and industry

• Organizations: IETF, BBF, MEF, ITU-T, LF, …• Other ETSI groups: NFV, ZSM, NGP, MEC, NTECH, OSM, …

• Cooperate with mainstream operators, vendors and research institutes in Europe, USA and Asia• Guide the industry with consensus on evolution of network intelligence

Further Opportunities in ENI and Related Organizations

Related Organization Activities

IETF-ANIMA ANIMA (Autonomic Networking Integrated Model and Approach) WG on autonomic networking integrated model and approach

GSMAGSM (Global System for Mobile Communications) Association issued a new report, ' Intelligent Connectivity: How the Combination of 5G, AI and IoT Is Set to Change the Americas’, highlighting how the region is set to benefit from the age of 'intelligent connectivity 'or the fusion of high-speed 5G networks, AI and the IoT.

ITU-T Focus on machine learning for future networks including 5G

TM Forum Proposed AI & Data Analytics Project including AI data model for Telco., Management standards for AI, Creating an AI data training repository, and AI maturity model and metrics.

CCSA TC610 AIAN (Artificial Intelligence Applied in Network) industry group & AIIA (Artificial Intelligence Industry Alliance)

IEEE-ETI ComSoc network intelligence Emerging Technology Initiative (ETI)

IRTF NMRG Network Management Research Group (NMRG) provides a forum for researchers to explore new technologies for the management of the Internet.

ITU-T FG-ML5G The Focus Group will draft technical reports and specifications for machine learning (ML) for future networks, including interfaces, network architectures, protocols, algorithms and data formats.

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

Linux FoundationONAP (Open Network Automation Platform) allows end users to design, manage, and automate services and virtual functions; PNDA (Platform for Network Data Analytics) is a platform for scalable network analytics, rounding up data from multiple sources on a network; Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps.

Contact Details

ENI Technical Manager:

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

Thank you!

ETSI ENI#9 meeting and workshop will be held by Samsung in

Warsaw, Poland, on 09-12 Apr, 2019.

You are welcome to join us!

Looking forward cooperation with ML5G and AIIA!