Computing Department A Utility-based QoS Model for Emerging Multimedia Applications Mu Mu, Andreas Mauthe Computing Department, Lancaster University Lancaster, UK Francisco Garcia Agilent Laboratories, UK 17 September 2015 International Workshop on Future Multimedia Networking - FMN’08

Computing Department 2 Challenges of QoS on Emerging Multimedia Application Utility-based QoS Model Simulation Conclusion and Future Work Agenda

Computing Department 3 Challenges of QoS on Emerging Multimedia Application Utility-based QoS Model Simulation Conclusion and Future Work Agenda

Computing Department Multimedia Applications: Emerging Multimedia Application Requirements on QoS model: QoS on Application/System/User Level (QoE) Real-time Diagnosis 4 System

Computing Department 5 Challenges of QoS on Emerging Multimedia Application Utility-based QoS Model Simulation Conclusion and Future Work Agenda

Computing Department NetworkSenderReceiver Direct Impairments Delay, Jitter, Loss Indirect Impairments Encoding Loss Quality Evaluation Quality Evaluation Delay Jitter Loss Bandwidth QoE Utility-based QoS Model 6

Computing Department Constructing Utility Functions System Utility Functions Application Utility Functions Flow Utility Functions Impairment Utility Functions From bottom to top 7 System

Computing Department Constructing Utility Functions Impairments Utility Functions 8 Empirical Functions Experiment Results

Computing Department Constructing Utility Functions System Utility Functions Application Utility Functions Flow Utility Functions Impairment Utility Functions From bottom to top 9 System

Computing Department Constructing Utility Functions Flow Utility Functions Weighted Sum: Minimum: User Preference, Flow Characteristic Product: 10

Computing Department Constructing Utility Functions System Utility Functions Application Utility Functions Flow Utility Functions Impairment Utility Functions From bottom to top 11 System

Computing Department Constructing Utility Functions Application Utility Functions User Preference, Application Characteristic The advantage factor “A”: additional human perception factor 12

Computing Department Constructing Utility Functions System Utility Functions Application Utility Functions Flow Utility Functions Impairment Utility Functions From bottom to top 13 System

Computing Department Constructing Utility Functions System Utility Function System utility function is introduced to evaluate the efficiency of resource utilization by user experience. 14

Computing Department Constructing Utility Functions 15 Monitoring and Evaluation Control and Management System Utility Functions Application Utility Functions Flow Utility Functions Impairment Utility Functions

Computing Department 16 Challenges of QoS on Emerging Multimedia Application Utility-based QoS Model Simulation Conclusion and Future Work Agenda

Computing Department 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 VoIP Pareto On/Off. FTP One way FTP agent. IPTV “Traffic Trace” Jurassic Park I H.263 VBR Online Gaming CBR stream.

Computing Department Simulation Scenario 18

Computing Department Simulation 19 Impairment Value Utility Value Application Utility Delay Jitter Loss

Computing Department Control and Management 20 Monitoring and Evaluation Simulation

Computing Department 21 Challenges of QoS on Emerging Multimedia Application Utility-based QoS Model Simulation Conclusion and Future Work Agenda

Computing Department 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

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Computing Department 24 Framework for the Integrated Video Quality Assessment quality of delivery artifacts measurement 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. }

Computing Department Simulation

Computing Department Simulation Traffic Generator VoIP We choose Pareto On/Off object to simulate traffic of VoIP application. packetSize_ 160 burst_time_ 500ms idle_time_ 50ms rate_ 68kb shape_ 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_ 20 Agent/TCP set windowInit_ 2 Agent/TCP set packetSize_ 1000 Agent/TCPSink set packetSize_ 40 IPTV NS-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-trace set t1 [new Application/Traffic/Trace] $t1 attach-tracefile $tfile _ Jurassic Park I H.263 VBR Online Gaming CBR stream with sending rate of 15kbit/s and packet size of 80 Bytes to simulate game traffic

Computing Department 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 27