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  • (1) expresses the total bandwidth demand of a single link at time t. (2), (3), (4) express the bandwidth demand of a single viewer at time t. (5) represents the assumption 2. (6) is the characteristic of probability function. (7), (8) satisfies the assumption 5 and Zipf ’ s law is used for the popularity of available program files. (9), (10) satisfies the assumption 4, the number of requests by active viewers follow Poisson distribution.
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    1. 1. Advanced Network Management (Fall 2007) Professor: James Won-Ki Hong Chen Bin Kuo (20077202) Young J. Won (20063292)
    2. 2. <ul><li>Revisited </li></ul><ul><li>VoIP, Data, & Other Traffic Models </li></ul><ul><li>TPS Models </li></ul><ul><li>IPTV Live Traffic Model </li></ul><ul><li>IPTV VoD Traffic Model </li></ul><ul><li>Conclusion </li></ul>05/19/10
    3. 3. 05/19/10 Details Bundled Services Traffic Selection & Survey on Previous Models Measurement Analysis on Selected Bundled Service Applications Measurement Paper (IPTV) Model Formulation Traffic Impact & Demand Analysis Final Report
    4. 4. <ul><li>Problem? </li></ul><ul><li>Lack of effective combinational models for analyzing traffic impact and demand in bundled services environment </li></ul><ul><ul><li>Traffic demand analysis in bundled services </li></ul></ul><ul><ul><li>Traffic impact analysis with existing traffic </li></ul></ul><ul><li>Goal? </li></ul><ul><ul><li>Traffic monitoring and analysis of bundled service applications </li></ul></ul><ul><ul><li>Traffic modeling formulation for combined traffic </li></ul></ul>05/19/10
    5. 5. <ul><li>Link performance summary </li></ul>05/19/10 <ul><li>Capture summary </li></ul>
    6. 6. <ul><li>The D&P type completes its download with initial traffic bursts </li></ul><ul><ul><li>The streaming type show low yielding bandwidth through the entire running time </li></ul></ul>05/19/10
    7. 7. <ul><li>Asymmetric traffic delivery pattern </li></ul><ul><ul><li>60~90 bytes in size for signaling purpose to acknowledge the stuffed (1500 bytes) video packets </li></ul></ul><ul><ul><li>Upload performance shows no impact on the overall quality of IPTV STB, unlike P2P IPTV </li></ul></ul>05/19/10
    8. 8. <ul><li>Traffic bandwidth fluctuation </li></ul><ul><ul><li>Watching two consecutive episodes </li></ul></ul><ul><ul><li>7 Mbps vs. 2 Kbps </li></ul></ul><ul><ul><li>No channel surfing traffic burst, unlike multicast model </li></ul></ul><ul><li>This implies that there could be much simpler representation of IPTV traffic models </li></ul>05/19/10
    9. 9. <ul><li>Measurement conditions </li></ul><ul><ul><li>Location: San Francisco, USA </li></ul></ul><ul><ul><li>Broadband access: Cable </li></ul></ul><ul><ul><li>STB: MegaTV </li></ul></ul><ul><ul><li>Viewing duration: 30 minutes / 120 minutes </li></ul></ul><ul><li>Quick observations </li></ul><ul><ul><li>Buffering delay in about every 5 minutes </li></ul></ul><ul><ul><ul><li>Occasional frame stoppage </li></ul></ul></ul><ul><ul><li>Below minimum throughput bound (3.5 Mbps vs. 6 Mbps) </li></ul></ul><ul><ul><li>Fluctuation vs. Constant </li></ul></ul>05/19/10
    10. 10. <ul><li>The bandwidth at the client domain does not correspond to the required SD or HD transfer ratios </li></ul><ul><li>The proposed formulas describe the IPTV VoD services by D&P delivery architecture which has not been proposed in any other work </li></ul><ul><li>The traffic burst due to channel surfing is negligible in this VoD architecture </li></ul><ul><li>The running time of each channel is fixed and known </li></ul><ul><ul><li>The occurrence of VoD traffic is not continuous but an independent discrete event </li></ul></ul><ul><li>Channel viewing time does not necessarily coincide with the packet transmission time between the server and STB </li></ul>05/19/10
    11. 11. 05/19/10 Initialization (viewers, program files) Choose viewers randomly (number of viewers is Poisson distribution) Viewers choose the program file (Zipf distribution) Calculate bandwidth demand from active viewers Check viewers states (downloading, playing, finishing) Simulation ends (time is up) 1 round = 1 minute
    12. 12. 05/19/10 <ul><li>Assumptions </li></ul><ul><ul><li>Viewer behavior is uniform </li></ul></ul><ul><ul><li>Once the viewer chose the program file, the viewer stayed until the program file ends </li></ul></ul><ul><ul><li># of requests per unit time by viewers follows Poisson distributio n </li></ul></ul><ul><ul><li>Popularity of program files follow Zipf distribution </li></ul></ul><ul><li>These can be more realistic if we have the real values. </li></ul>Parameter Description Maximum active viewers 200 Mena number of requests by viewers 5 (request/minute) Total Simulation Duration 200 minutes File size (Mbytes) 2000 1500 1000 500 250 200 200 200 200 200 Playing Duration (Minutes) 120 80 60 30 15 10 10 10 10 10 Popularity 11.38% 17% 34.1% 8.53% 6.82% 5.7% 4.88% 4.27% 3.8% 3.4% Access Network FTTB FTTH xDSL Cable Downloading Rat e (Mbps) 10 11 6 7 Popularity 20% 10% 60% 10%
    13. 13. 05/19/10
    14. 14. <ul><li>Multicast Live TV simulation vs. VoD simulation </li></ul><ul><ul><li>Avg. 1000 Mbps vs. Avg. 550 Mbps for 200 viewers </li></ul></ul><ul><ul><li>Over 2 hours (multiple channel viewings) </li></ul></ul><ul><li>Take the average of multiple simulations </li></ul><ul><ul><li>Test 1-4 and its average </li></ul></ul>05/19/10
    15. 15. 05/19/10 TPS Components VoIP VoIP+Data Data HTTP IPTV Others Available Choices 1 5 6 1 2 2 2 3 4 3+4 6 Number Methodologies 1. Market projection based 2. Peak-to-average analysis 3. Multicast Live Demand Model 4. VoD Demand Model 5. VoIP Demand Model (Erlang-B,C) 6. Constant Rate
    16. 16. <ul><li>VoIP+Data, IPTV </li></ul><ul><ul><li>Market-based approach, Multicast demand model </li></ul></ul><ul><ul><li>Market-based approach, VoD demand model </li></ul></ul><ul><ul><li>Market-based approach, Multi + VoD demand model </li></ul></ul><ul><li>VoIP, Data, IPTV </li></ul><ul><ul><li>Call model, Peak-to-average analysis, Demand model </li></ul></ul><ul><li>VoIP, HTTP, UDP, IPTV </li></ul><ul><ul><li>Call model, Peak-to-average analysis, Constant, Demand model </li></ul></ul><ul><li>From two perspectives </li></ul><ul><ul><li>Edge, Backbone </li></ul></ul><ul><ul><li>Minimum and Maximum bound </li></ul></ul><ul><ul><li>Data-dominated  IPTV-dominated </li></ul></ul>05/19/10
    17. 17. <ul><li>Contributions </li></ul><ul><ul><li>A guideline on what to look for and how to handle TPS or beyond traffic demand analysis </li></ul></ul><ul><ul><li>A concrete and available set of models are explained </li></ul></ul><ul><ul><li>Previous VoIP, Data, IPTV, and TPS as a whole for bandwidth demand models </li></ul></ul><ul><ul><li>Our proposed models for IPTV and simulation </li></ul></ul><ul><ul><li>Combinational model examples </li></ul></ul>05/19/10
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