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Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641

Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641. Image from: https:// www.tpg.com.au. Outline. Introduction System architecture System implementation Used cases Conclusion. Internet Protocol Television (IPTV). Voice Service. IP Network. TV Service. Data Service.

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Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641

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  1. Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641 Image from: https://www.tpg.com.au

  2. Outline • Introduction • System architecture • System implementation • Used cases • Conclusion

  3. Internet Protocol Television (IPTV) Voice Service IP Network TV Service Data Service Image from: http://joannekraft.com

  4. IPTV Monitoring Set Top Box STB STB Server SNMP Trap Server SNMP Agent SNMP Agent • Collect data • Queue management • Filter and Parsing • Store • Data Source • Periodical • Triggered by user • (Channel Zapping)

  5. System Architecture

  6. System Implementation • Data Source (SNMP Traps) Network Processing  Network level Video decoding  Application level • Control using SNMP

  7. System Implementation • Data Volume • 180 Bytes/msg, 100,000 subscribers

  8. System Implementation • Data Volume • 180 Bytes/msg, 100,000 subscribers

  9. System Implementation (server side)

  10. System Implementation (server side) • Data Analysis • Diagram for historical data • Diagram for real time data Database APP Queries Data

  11. Use Cases • Application-Level IPTV Quality Monitoring • Integration with Customer Support • Network Topology Mapping • Error Localization • Correlation with Weather Phenomena

  12. App-level IPTV Quality Monitoring Establish a baseline level of application-level metrics and network-related metrics Detect any significant increase in errors = Experience of low quality

  13. Integration with Customer Support Usual Case Cost long time to describe the problems, and very likely in a wrong way!! After Integration • Advantage: • Guide further decisions to mediate the problem • Shorten the delay between a decision and its results

  14. Network Topology Mapping Unavailable precise network topology map  create network graph using IP addressing hierarchy It allows visual exploration of network hierarchy and quick identification of problematic nodes by their color.

  15. Error Localization Heat map of error severity visualize the percentage of errors well suited for visual analytics allow the patterns to be discovered quickly Horizontal streaks Long running underperformance of an individual BNG Vertical streaks A connection between independent BNGs Or a similar usage pattern

  16. Correlation With Weather Phenomena Natural causes Lightning strikes Create a large amount of impulse noise IPTV systems without FEC are especially susceptible to such disturbance Highly localized and little can be done Weather Radar Map Explain away the unavoidable and focus on the preventable

  17. Conclusion • Other cases Conveys information about how the subscribers use and interact with the IPTV system Rate for individual TV shows & Imply undesirable contents • Future work Automation The personal TV activity data could in the future be stored without anonymization.

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