soldani_huawei_bridgingqoeandqos

7/29/2019 soldani_huawei_bridgingqoeandqos

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HUAWEI TECHNOLOGIES CO., LTD.

Security Level:

www.huawei.com

Bridging QoE and QoSfor Mobile BroadbandNetworksDr. David SoldaniVP Huawei European Research Centre

21-22 September 2010

http://www.etsi.org/WebSite/NewsandEvents/QoSQoEUserExperience.aspx


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HUAWEI TECHNOLOGIES CO., LTD. Page 2

Curriculum Vitae Dr. David Soldani

15 years in ICT industry

2009 present: Huawei Technology Dsseldorf

- VP European Research Centre

- Head of IP Transformation Research Centre (IPTRC)

2007 2009: Nokia Siemens Networks (NSN)- Head of Solutions & Services Innovation

- Head of Customer Networks & Solutions (CTO office)

1997 2007: Nokia (Finland and Italy)

- Various technical & research management positions

1995 1997: Military Navy, Sirti SpA, Rohde & Schwarz

- Various technical positions

Areas of Expertise (not exhaustive)

Lead R&D and Customer Services organizational units

Unit/area strategy formulation and implementation

Technology and Innovation Management for ICT industry

Conduct lectures at Universities, Military Academy and ICT Companies

Perform advanced research in the fields of own expertise

Provide consulting functions to profit and nonprofit organizations

Supervise any type of R&D deliverables

Published/presented many international papers

Editor in chief and one of the main contributors to several books

Holder of several international patents

Relevant Experience (not exhaustive)Professional Background

Solutions for Traffic Management in Mobile Broadband Networks

Mobile Broadband Networks (TETRA, GSM, EDGE, WCDMA, HSPA,

LTE/SAE and WiMAX)

E2E QoS, QoE and Policy Based Management Solutions

E2E Service and Network Performance, Network Planning,

Optimization and Automation

Transport Network Layer Technologies (IP/MPLS/Ethernet)

Fixed Broadband Networks (xDSL, xPON)

Dr. David SoldaniVP European Research Centre

HUAWEI TECHNOLOGIES CO., LTD.Huawei Technologies Duesseldorf GmbHRiesstr. 25, D - 3.0G, 80992 Munich, Germany

Tel: +49-89-1588344095

Fax: +49-89-1588344446

Mobile: +49-1622047695

E-mail: [email protected]


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Outline

Definitions and industry trends

Objectives of monitoring QoE indicators

Solution layered architecture

Bridging QoE and QoS

Reference case

Conclusions


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Definition: Quality of Experience (QoE)

Measurement of how well that

network is satisfying the end

user's requirements Includes the complete end-to-

end system effects (client,

terminal, network, services

infrastructure, etc.)

QoE is also a consequence of

a users internal state (e.g.

expectations), thecharacteristics of the

designed system (e.g.

functionality) and the context

(or the environment) within

which the interaction occurs5.Excellent

4.Good

3.Fair

2.Poor

1.Bad

The overall acceptability of an application orservice, as perceived subjectively by the end-user.by ITU-T P.10/G.100


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QoS and QoE parameters Mapping Model

Subjective

Measurements

Objective

Measurements

QoS QoE

SQoS

(Network based)e.g. Packet Error Loss Ratio

ESQoS

(Service based)e.g. Web Page Loading Time

MOS

(Mean Opinion Score)

Objectiveevaluation

Subjectiveevaluation

Viable solutionSQoS = System QoS

ESQoS = E2E Service QoS


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2020 Global Broadband Revenues Forecast

Source: Mobile, Fixed and Wholesale Broadband Business Models, Telco 2.0, June 2010

Increase of 52% in revenue and more than half the revenue growth will come fromwholesale and two-sided fees for improved access capacity and quality

Current forecast expects mobile data traffic to grow by 300x to 500x over the next 10years: mobile broadband will be worth $138bn (32% of total broadband revenues)

New upstream customers are forecast to generate over $90 billion in broadbandrevenues globally by 2020


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1. Offloading to WiFi2. Offloading to Femtocell3. Radio network enhancements

1. Signaling management

2. Macro network offload

3. Radio packet scheduling and prioritization

4. Compression, adaptation and transcoding Compression

Rate-adaptation

Network-sharing

5. Device-based traffic management techniques6. Contention management & tuning TCP/IP7. Deep packet inspection, policy-based traffic shaping & differential

charging8. End-to-end service assurance and monitoring9. Caching, multicast & CDNs10. Congestion APIs

Key Technologies for Mobile BroadbandTraffic Management

Source: Disruptive Analysis, June 2010

In many operators,

there is often no

single individual who

"owns" the issue of

data traffic, who can

develop a holistic

solution

In many operators,

there is often no

single individual who

"owns" the issue of

data traffic, who can

develop a holisticsolution

8. End-to-end service assurance and monitoring


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QoE/QoS aspects addressed by all importantstandardization bodies

MobileVoice

VOIP MTVIPTV Data

Framework

E2E Servicemetrics

Systemmetrics

MeasurementMethod

GB934 GB938

TR 26.944

TS 102 250 -3/4

TS 101 329 - 5

P.NAMS PEVQ

G.1070

P.861-862 P.563

G.107seriers

Y.1541

E.800

TS 32.410

TR 32.814

MDI

Y.1541

TS 32.425

Improving QoE of RT

Communication Services

ETSI Specialist Task Force 354

UE satisfaction criteria

TS 102 250-2

KQI for PS services

ETSI STQ

TR-126

EG 202 670


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Audio QoE: the state of art

P.564

G.107E-Model

1996 1998 2002

ETSI ETR 250

TS 101 329-5

P.861PSQM

P.862PESQ

P.563

ITU P.800

P.562

P.561

P.OLQAP.CQO

2005

PESQ: intrusive method that compares the degraded signal to the original signal

P.563: non-intrusive method and does not need the original signal

P.564: model for assessing voice over IP transmission quality

E-Model: non-intrusive method and its extensions in under study (2009-2012) P.OLQA: evolution of PESQ and it is still under research (2009-2010) P.CQO: new model based on P.561, P.563, P.564 and E-Model currently under study (2009-2012)

PESQ: intrusive method that compares the degraded signal to the original signal

P.563: non-intrusive method and does not need the original signal

P.564: model for assessing voice over IP transmission quality E-Model: non-intrusive method and its extensions in under study (2009-2012) P.OLQA: evolution of PESQ and it is still under research (2009-2010) P.CQO: new model based on P.561, P.563, P.564 and E-Model currently under study (2009-2012)

E-Modelis supported by Huawei SQM/QoE products E-Modelis supported by Huawei SQM/QoE products

E-ModelExtension


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Video QoE: the state of art

ReducedReference

Non ReferenceFull ReferenceSubjectiveEstimation

Imageresolution

ITU-T J.246

ITU-T J.147

ITU-T J.249

VQEG:

MM Project

VQEG:

RRNR-TV

HDTVITU-T SG12:P.NAMSP.NBAMS

G.OMVASITU-T P.910

ITU-T P.911

ITU-R BT.500

ITU-T J.140

ITU-T J.245

QCIF

CIF

VGA

HDTV

SDTV

ITU-T J.247

ITU-T J.144

ITU-R BT.1683

Completed Ongoing projects

P.NAMS (from packet header) and P.NBAMS (from payload information): non-reference

multimedia, audio and video quality estimation methods G.OMVAS defines a Quality Planning Tool (E-Model) for IPTV services (by 2011) ITU-T also has several IPTV QoE projects, such as G.IPTV_MMRP, G.IPTV_PMP.

P.NAMS (from packet header) and P.NBAMS (from payload information): non-reference

multimedia, audio and video quality estimation methods G.OMVAS defines a Quality Planning Tool (E-Model) for IPTV services (by 2011) ITU-T also has several IPTV QoE projects, such as G.IPTV_MMRP, G.IPTV_PMP.

Huawei has a non-reference video quality estimation method, called MOS-V, it is a contribution to

P.NAMS, and it is supported by Huawei SQM/QoE products

Huawei has a non-reference video quality estimation method, called MOS-V, it is a contribution to

P.NAMS, and it is supported by Huawei SQM/QoE products


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IEEE special issue on QoE- March 2010 -

IEEE Network Special Issue on Improving Quality of

Experience for Network Services

Guest Editors

Dr. Jahan A. HassanUniversity of New South, Wales, Australia

Prof. Sajal K. DasUniversity of Texas at Arlington, USA

Prof. Mahbub Hassan,University of New South Wales, Australia

Dr. Chatschik BisdikianIBM T. J. Watson Research Center, USA

Dr. David SoldaniHuawei Technologies Co.,Ltd., Germany

http://dl.comsoc.org/livepubs/ni/public/2010/mar/index.html


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Outline





Reference case

Conclusions


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Operators challenges Very little work available on end user perception of the

services, especially from Internet, that are being offered

Gaps between network measured quality and userperceived experience

Mechanisms, procedures and tools to continuously

monitor, operate and report QoE indicators

Bandw

idth

End to End

Effects!

1

Cause!

User plane

Control plane

2


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Main objectives

1. QoE monitoring as support for strategiccompany decisions

2. QoE monitoring for network optimization,

supervision and operation3. Support for business and customer intelligence

processes


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Outline





Reference case

Conclusions


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HUAWEI TECHNOLOGIES CO., LTD.Page 16

Functional architecture

Accessnode

Accessnode

Core Networknode

Core Networknode

Aggregationnode

Aggregationnode

Terminaldevice

Terminaldevice

EmbeddedAgents

Network Elementsand Interfaces Probes

Network ElementsPM and FM Data

CorenodeCorenode

Service corenode

Service corenode

Data AcquisitionData AcquisitionLayerLayer

Data AnalysisData Analysis

LayerLayer

Data PresentationData PresentationLayerLayer

Data Query, Filtering, Refinement,Correlation and Processing

System QoS, Alarmsand Events

Network Model, Configurationand Topology

E2E Service QoSand Events Data CollectionData Collection

LayerLayer

MBB QoE Indicatorreports

MBB QoE NetworkOptimization, Supervision

and Operation

Customer and BusinessIntelligence

FunctionalFunctionalObjectives/Use CasesObjectives/Use Cases

Data Visualization and Report Generation

Mediation layer

Mediation layer (Optional)

Network BasedNetwork BasedApproachApproach

Terminal BasedTerminal BasedApproachApproach


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Functional elements (layers)

Data acquisition layer All relevant information related to QoE monitoring and measuring

Network and Terminal based approaches

Data collection layer Storage for vertical solutions with continuous monitoring

Real time (seldom) and periodic monitoring

Scale of samples across network and customers

Data analysis layer Format and data conversion required for different data sources

Data query, filtering, refinement, correlation and processing

Data aggregation, correlating and processing layer

Storage for horizontal solution

Presentation layer Tools for visualization and report generation

Continuous tracking of mobile broadband experience

Customer experience as a combination of network indicators


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Solution segmentation

ServicesServices Web surfing Streaming File DL/UL E-mail

Network ScopeNetwork Scope

Whole Network

Per region

Per NE/Cell

Per RAN Vendor

DeviceDevice

All devices

Device class

Device type

Device group

TechnologyTechnology

All technologies

2G

3G

CustomerCustomer

All customers

Segment

Tariff

Filtering based on all possible combinations


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Layered approach

Throughput ofservice C of user X,

Packet drop ratio,

Throughput ofservice B of user X,

Packet drop ratio,

Streaming

etc

VoIP

Email SessionSetup Success RatioEmail Session

Setup time

Email

Session SetupSuccess RatioActive Session

Throughput

FTP download

Web PageLoading Time

Active Session

Throughput

SDU Throughput, SDU Error Ratio,

SDU Transfer Delay Per User,

QoS class, etc.

Look at the pipe

(a PDP context)

Monitored ParametersSegmentation / Granularity

Gross and net throughput at each interface (cell, Iub, ),

Wide band received and transmitted power (cell), etc.Per Cell, Itf,

NE, etc.

Look at the

infrastructure

Throughput ofservice A for user X,

Packet drop ratio,

Per service,

User, etc.

Look shallowly

into the pipe

Web Browsing

ServiceQuality

Look deeply

into the pipe

Pipe = Bearer service

Mobile Broadband Network


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Huawei Solution for 3GPP Packet

Switched Networks

Backhaul

MSBTS

NodeB RNCSGSN

Internet/

Intranet

HLR

Iub Iu-PSGn

GiGrGb

Datacard

eNB

eNB

X2S1-MME

S1-U

S4

S5 S6a

S7

S11

S3

SGi

LTE UE

Uu

Data acquisition from NE & OSS

FM / PM and 3GPP NE

embedded probes

Data acquisition from Interface

Data acquisition from transport NE

Transport NE embedded

and interface probes

Data acquisition from Terminal

Device

manager

Service Quality Manager

3r party

Backbone

GGSN

MME

Serving GW PDNGW HSS

PCU

PCRF

Abis


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Outline





Reference case

Conclusions


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User and service satisfaction criteria

Gold, Silver and Bronze usersG, S, B User active throughput >

User_BW AND

Session success rate >

User_Target_SSR

User

bandwidth

8

7

6

5

4

3

2

1

QoS class*

(i)

Air interface (IP) packet error

loss rate < User_PELR(i)AND

98-percentile delay

User_target_rate(i)AND Video (Buffered Streaming), TCP-based(e.g., www, e-mail, chat, ftp, p2p file sharing,

progressive video, etc.)

Voice, Video (Live Streaming), Interactive

Gaming

Non-GBRVideo (Buffered Streaming), TCP-based(e.g., www, e-mail, chat, ftp, p2p file sharing,

progressive video, etc.)

IMS Signalling

Non-Conversational Video (Buffered

Streaming)

Real Time Gaming

Conversational Video (Live Streaming)GBR

Conversational Voice Air interface (IP) packet loss

error rate < User_PELR(i)AND

98-percentile delay 64 kb/s


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Example of Video clips: qualityaffected by packet loss

QoE scores of a video clip repaired with two loss concealmentschemes at various packet loss rates

PELR < 10-6PELR < 10-6


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Encoder H.264 with one IP packet = 7 188 Bytes

With 100 kb/s and PERL 10-6 PLF = 1.2x10-4

Example of video quality of IPTVServices: quality affected by packet loss


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Example of service satisfaction criteria

Web Service Web Paging Loading Time AND

Active Session Throughput

Video Streaming Streaming Mean Active Throughput AND

Streaming Media Active Transport Jitter AND Streaming Media Loss

Video via HTTP Active Session Throughput

VoIP Call Setup Success Ratio AND

Call Setup Time AND

Call Cut-off Ratio AND

Speech Quality


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Satisfaction estimation using Fuzzy Logic

0

1

ba KPI

Truth (KPI)

0

1

Truth (WPLT)

0

1

Truth (WPLT)

Air Interface Packet Error Rate (%) 98th-Percentile of Delay(ms)

Truth (PD)Truth (PELR)

1 100

Satisfaction = min [Truth (Air Interface Packet Loss Error Rate), Truth (98th-Percentile of Delay )]

Truth Value of a KPI (denoted by

weight) is given by

Maximum of truth values, if logical ORis used

Minimum of truth values, if logicalAND is used

Example for VoIP:


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Generic Truth function ofa QoS parameter:

Ex. Truth value (weight) of QoE for Web Browsing:

0.9384Active Session Throughput (kb/s)

0.30.33Web Page Loading Time (s)

Weight (Satisfaction)TruthValueKPI

QoE = Exp(- QoS) +

Satisfaction = min [Truth (Web Paging Loading Time), Truth (Active Session Throughput)]

Configurable parameters: , , , x1 andx2

Needs to benormalized


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Outline





Reference case

Conclusions


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Service quality differentiated G/S/B portfolio

33

Service Quality Differentiation in Mobile Broadband portfolioService Quality Differentiation in Mobile Broadband portfolio

Bronze Tariff A

Silver Tariff B

Gold Tariff C

Ultimate(e.g. HD Video)

Best for Social Media(e.g. facebook, youtube etc.)

Best for Surf & Chat

Service Quality Differentiation

Max Mbps DL/Mbps UL

Avg. Mbps DL/ Mbps UL

FUP / SSD Montly fee


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Bandwidth levels

(In loaded networks, e.g., throttling of Streaming and/or HD Video services for Bronze will make it possible to allocate the freed

capacity to Gold, or Silver, so that a higher priority will enable a better user experience.)

Empty network

Bronze Silver GoldBronze Silver Gold

Streaming SurfingHD videoAllocated capacity:

Tariff

Speed

Tariff

Speed

Loaded networkEmpty network

Bronze Silver GoldBronze Silver Gold

Streaming SurfingHD videoAllocated capacity:

Tariff

Speed

Tariff

Speed

Loaded network

(1) Surfing

(2) http Streaming

(3) RTP Streaming

(4) E-Mail

(5) File DL

(6) Other

Example ofMapping for Gold:


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QoS benefits

Dissatisfied

Users (%)

Traffic

(variable)

10%

With QoS

on (at cost X)

All on BE (no QoS,

with Resources R1)

QoS Gains = T2-T1 (at cost X,

for paying the QoS features,

with resources R1)

Traffic = T2

Traffic mix = Tm

(held constant)

0%

Traffic = T1

Worst

performing

service Satisfied Users(%)

Resource

(variable)

90%Over

provisioning

Additional Resources R2, at

cost Y, for accommodating

the same traffic as with QoS

on, satisfactorily

Traffic = T2

(both held constant)

Traffic mix = Tm

0%

Profit = Y-X > 0

Over provisioning = R2-R1Worst

performing

service

Total profitTotal profit

1) Benefit of radio, transport andgateway QoS functions for HSPA andLTE/SAE

2) Corresponding cost savingswith respect to over provisioning

+ additionalrevenues!

RNC

SGSN&GGSN

Web/Streaming/E-mail/FTP/MMS

server

TrafficGenerator

Downlinktraffic

Node B


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Traffic Mix

70%30%Without QoS

60%30%10%With QoS

BronzeSilverGoldUser Mix

35%5%3%7%15%35%

OtherFile

downloads

e-mailRTP

streaming

http

streamingSurfing

Service Mix


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User satisfaction criteria for HSPA

NANANAWeb page

delay

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