computing department a utility-based qos model for emerging multimedia applications mu mu, andreas...

Computing Department

A Utility-based QoS Model for Emerging Multimedia Applications

Mu Mu, Andreas MautheComputing Department, Lancaster University

Lancaster, UK

Francisco GarciaAgilent Laboratories, UK

21 April 2023

International Workshop on Future Multimedia Networking - FMN’08


2

Challenges of QoS on Emerging Multimedia Application

Utility-based QoS Model

Simulation

Conclusion and Future Work

Agenda


3



Simulation


Agenda


Multimedia Applications:

Emerging Multimedia Application

Requirements on QoS model:

• QoS on Application/System/User Level (QoE)

• Real-time Diagnosis

4

Application 1

Flow 1 Flow 2 Flow N

Application N

End System

QoS Requirements

QoS Requirements

QoS Requirements

System


5



Simulation


Agenda


NetworkSender Receiver

Direct Impairments

Delay, Jitter, LossIndirect Impairments

Encoding Loss

Quality

Evaluation

Quality

Evaluation

DelayDelay

JitterJitter

LossLoss

BandwidthBandwidth

QoEQoE


6


Constructing Utility Functions

System Utility Functions

Application Utility Functions

Flow Utility Functions

Impairment Utility Functions

From bottom to top

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B

7

System


Constructing Utility FunctionsImpairments Utility Functions

8

Empirical FunctionsEmpirical Functions

Experiment Results

Experiment Results

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B







From bottom to top

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B

9

System


Constructing Utility FunctionsFlow Utility Functions

Weighted Sum:

Minimum:

User Preference, Flow CharacteristicUser Preference, Flow Characteristic

Product:

10

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B







From bottom to top

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B

11

System


Constructing Utility Functions Application Utility Functions

User Preference, Application CharacteristicUser Preference, Application Characteristic

The advantage factor “A”: additional human perception factorThe advantage factor “A”: additional human perception factor

12

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B







From bottom to top

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B

13

System


Constructing Utility Functions System Utility Function

System utility function is introduced to evaluate the efficiency of resource utilization by user experience.

14

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B


QoS Control SingalingControl and

ManagementSummarized Flow Specification and Evaluation Results

Oth

er C

MS


15

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B

Monitoring and Evaluation

Control and Management

Application 1

Flow 1

Application N

End System

D J L B

Flow 2

D J L B

Flow N

D J L B






16



Simulation


Agenda


Simulation

• Goals:

Demonstrate the behavior of the single-end monitoring and evaluation system.Demonstrate the way utility-based method can be used to support QoS management.

• Simulation Tools:

Network Simulator 2

• Traffic Generator

17

VoIPPareto On/Off.

FTPOne way FTP agent.

IPTV“Traffic Trace”

Jurassic Park I H.263 VBR http://www.tkn.tu-berlin.de/research/trace/trace.html

Online GamingCBR stream.


SimulationScenario

182 50 100200 300 500 700 800

Home 2 VoIP

Home 1 VoIP

Home 1 Online Gaming

Home 2 Online Gaming

Home 1 IPTV

Home 2 IPTV

Home 1 FTP

NS2 Simulation Time (s)

Backbone

2 5

TCP

FTP

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 1 1

a0

5

TCP

FTP

4

UDP

Game

3

UDP

IPTV

ISP 1

b1

1

2

UDP

VoIP

Home 4

1

b0

a1

100G 5ms Queue 40

1000G 30ms Queue 20

2.5Mb 15ms Queue 10

6Mb 5ms Queue 20

1.5Mb 35ms Queue 15

2

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 2

1

100M 5ms Queue 20

25M 10ms Queue 10


Simulation

Backbone

2 5

TCP

FTP

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 1 1

a0

5

TCP

FTP

4

UDP

Game

3

UDP

IPTV

ISP 1

b1

1

2

UDP

VoIP

Home 4

1

b0

a1

100G 5ms Queue 40

1000G 30ms Queue 20

2.5Mb 15ms Queue 10

6Mb 5ms Queue 20

1.5Mb 35ms Queue 15

2

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 2

1

100M 5ms Queue 20

25M 10ms Queue 10

19

Impairment Value Utility Value

Application Utility

Delay

Jitter

Loss

Computing Department Control and Management

Backbone

2 5

TCP

FTP

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 1 1

a0

5

TCP

FTP

4

UDP

Game

3

UDP

IPTV

ISP 1

b1

1

2

UDP

VoIP

Home 4

1

b0

a1

100G 5ms Queue 40

1000G 30ms Queue 20

2.5Mb 15ms Queue 10

6Mb 5ms Queue 20

1.5Mb 35ms Queue 15

2

UDP

VoIP

4

UDP

Game

3

UDP

IPTV

Home 2

1

100M 5ms Queue 20

25M 10ms Queue 10

20

Monitoring and Evaluation

Simulation


21



Simulation


Agenda


Overview and Future Works

• Things that we’ve achieved:

1. Analysis of emerging applications on IP Network

2. A Design of a QoS Model

3. Modeling user experience on hierarchical application with utility functions

4. Outlook of utility-based QoS management

• Things for future study

1. Utility-based QoS management

2. Improve application QoS model

3. Integrate NG QoS model into existing/future network architecture

22

Computing Department23


24

Framework for the Integrated Video Quality Assessment

Quality Estimator

Network Analyzer

Packet Inspector

Artifacts Measurement

Artifacts Prediction

PacketDecoderVideo

QualityScore

qual

ity o

f del

iver

y

artif

acts

mea

sure

men

t

Use Case:If (PLR<=threshold_1) { QE simply announces the quality as “perfect” so no more assessment actions are required.}elseif (threshold_1<PLR<threshold_2){• QE can use PLR in relation to video entropy, content characteristics, encoding schemes and other additional information for extended assessment.• QE uses artifact prediction or artifact measurement results for more precisely prediction of video quality.}else {QE Instantly determine the quality as “out-of-services” or “bad” and notify the service provider.}


Simulation

21.04.23 25


SimulationTraffic Generator

• VoIP• We choose Pareto On/Off object to

simulate traffic of VoIP application.

• packetSize_ 160burst_time_ 500msidle_time_ 50msrate_ 68kbshape_ 1.5

• FTP• We choose one way agent because in this

simulation we only analyze the traffics toward Home 1 and Home 2.

• Agent/TCP set window_ 20Agent/TCP set windowInit_ 2Agent/TCP set packetSize_ 1000Agent/TCPSink set packetSize_ 40

IPTVNS-2 offers “Traffic Trace” as a type of application. Traffic Trace reads the binary trace file and generate packets as defined in it. set tfile [new Tracefile]$tfile filename example-traceset t1 [new Application/Traffic/Trace]$t1 attach-tracefile $tfile

_Jurassic Park I H.263 VBR

http://www.tkn.tu-berlin.de/research/trace/trace.html

Online GamingCBR stream with sending rate of 15kbit/s and packet size of 80 Bytes to simulate game traffic.

21.04.23 26


Challenges of Traditional QoS Model

Overprovisioning + always an solution - bandwidth != service assurance

Integrated Services+ end-to-end - too much state information - RSVP

Differentiated Services+ easy to implement - number of class for ToS - cross ISP

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