soldani_huawei_bridgingqoeandqos
Transcript of 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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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
Definitions and industry trends
Objectives of monitoring QoE indicators
Solution layered architecture
Bridging QoE and QoS
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
Definitions and industry trends
Objectives of monitoring QoE indicators
Solution layered architecture
Bridging QoE and QoS
Reference case
Conclusions
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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
Definitions and industry trends
Objectives of monitoring QoE indicators
Solution layered architecture
Bridging QoE and QoS
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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Bridging QoE and QoS
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
Definitions and industry trends
Objectives of monitoring QoE indicators
Solution layered architecture
Bridging QoE and QoS
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