slicenet.eu

End to End Cognitive Slicing

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IMEN GRIDA BEN YAHIA AND JOANNA BALCERZAK

ORANGE LABS NETWORKS, FRANCE

Outline Project ID card & Objectives

SliceNet Overview Information Model

Management Architecture

Stakeholders

SliceNet Use cases & Metrics Use case overview

QoE, QoS for slicing

Zoom on eHealth

Perspectives & conclusion

Call H2020-ICT-2016-2017

Topic: ICT-07-2017

Project type : RIA

Proposal number: 761913

Project duration: 36M

Budget:7.979.030 Euros

SLICENET: End-to-End Cognitive Network Slicing and Slice Management Framework in

Virtualized Multi-Domain, Multi-Tenant 5G Networks

Project ID Card

5G slice management and orchestration architecture for vertical businesses

Vertical-tailored control exposure of network slices

Defining cognitive techniques for Verticals request mapping, Slice resource optimization, QoE & QoS mapping

Setting transverse and common management building blocks across different vertical use cases

Project High level objectives

Outline

Project ID card & Objectives

SliceNet Overview Information Model

Management Architecture

Stakeholders

SliceNet Use cases & Metrics Use case overview

QoE, QoS for slicing

Zoom on eHealth

Perspectives & conclusion

Information Model Concepts

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Co

nce

pts

Le

vels

1) Service Slice Resources

2) Service Level

3) Service Slice Level

4) Slice Resource Level

1) Service Slice Resources

2) Service Level

3) Service Slice Level

4) Slice Resource Level

1) Service Slice Resources

2) Service Level

3) Service Slice Level

4) Slice Resource Level

1) Service Slice Resources

2) Service Level

3) Service Slice Level

4) Slice Resource Level

SliceNet Slice Template (excerpt)

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Class name Use case Smart City

Slice Type URLLC, eMBB, etc. mMTC

Network Performance Commited Bandwith per endpoint - DS

Commited Bandwith per endpoint – US

Total Slice Bandwidth – DS

Total Slice Bandwidth – US

50kbps

50kbps

the number of endpoints multiplied by the

committed bandwidth per endpoint(endpoints

communicate simultaneously)

Priority levels Latency – peak

Latency – mean

Jitter – peak

Jitter – mean

Packet loss - without

<1000ms

<500ms

300ms

100ms

<1%

Plug & Play feature Monitoring only

NFs configuration

QoS/QoE control

SDN forwarding

NFs lifecycle

Slice lifecycle

Monitoring only

Plug & Play view Service level

Slice level

NF level

Service level view

SliceNet Management & Orchestration

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Co

gnit

ion

Su

b-p

lan

e

Orchestration Sub-plane

SS-O

NMR-O

Vertical

NFVI-PoP

P&P

Manager

NFVI-PoP NFVI-PoP NFVI-PoP

SliceNet Orchestration

multi-domain interactions

Service coordination

Slice orchestration

Resource orchestration

NFV-NS orchestration

Vertical Vertical

Mo

nit

ori

ng

Sub

-pla

ne

Infrastructure Sensors

…

Resource Monitoring

Raw resource, VNF,

PNF metrics/statistics

… …

Topology Monitoring

Network graph and UE

location

Traffic Monitoring

Traffic patterns and

characteristics

Slice Monitoring QoS Sensor

Slice end-to-end QoS metrics

Service Monitoring QoE Sensor

Service-level QoE

metrics/KPIs

Information Sub-plane

ST Catalogue

SD Catalogue

SI/NSI Inventory

NST Catalogue

Aggregation

Data Cleaning

Data Pre-processing

Data Storage

Analytics

Model Selection

Analysis Analysis

Analytic Engine

Model Training

Model Scoring

Policy Framework

Policy Recommender

Policy Repository

Policy Distribution

Model Inference Optimiser

Processing Engine

SliceNet Stakeholders

DSP & NSP Standalone Actors

DSP & NSP Combined Actors

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Network Service Provider (NSP),

Digital Service Provider (DSP),

Digital Service Customer (DSC)

Outline

Project ID card & Objectives

SliceNet Overview Information Model

Management Architecture

Stakeholders

SliceNet Use cases & Metrics Use case overview

QoE, QoS for slicing

Zoom on eHealth

Perspectives & conclusion

Use case overview

eHealth UC

Enhanced Mobile

Broadband

Smart Grid UC

Ultra Reliable Low Latency

Communications

Smart City UC

Massive Machine Type

Communications

Aveiro, Portugal Cork, Ireland Alba Iulia, Romania

Smart Grid UC overview

Smart Grid UC

Ultra Reliable Low Latency

Communications

Aveiro, Portugal This UC relies on ultra-low latency (mesh) communications between the power grid sensors/actuators (IED - Intelligent Electronic Devices). It also requires high density of network communications coverage, as well as with high quality (in terms of ultra-low latency capacity). Based on these requirements, the most critical aspect of the UC is the RAN coverage.

eHealth UC overview

eHealth UC

Enhanced Mobile

Broadband

Cork, Ireland

Smart City UC overview

Smart City UC

Massive Machine Type

Communications

Alba Iulia, Romania

E2E cognition requires well defined QoE and QoS to be granted

QoE definition QoS definition

Ref. by ITU –T P.10/G.100 Ref. by ITU –T E.800

QoE, QoS

QoE is therefore influenced by the delivered QoS (measurable with objective metrics) and the psychological factors influencing the perception of the user. The same QoS level might not guarantee the same QoE level for two different users.

Quality of Service is defined as the totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service.

Quality of Experience has been defined as the degree of delight or annoyance of the user of an application or service.

QoE, QoS life Cycle

Contact: Mark Roddy – cork institute of technology

Zoom on eHealth use case

Zoom on eHealth use case

Contact: Mark Roddy – cork institute of technology

Zoom on eHealth use case

Contact: Mark Roddy – cork institute of technology

Takeaway message

Towards E2E Cognitive Mgt. for slicing we need to revisit QoE, QoS for slicing

Identify the main management building blocks

On defining the slice template, data model for each use case

Endorse the work on data association, fusion, correlation and aggregation

Set up of integrated testbed allowing multi level orchestration and cognitive operations for the three project use cases and beyond.

Thank You !!!

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