Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641
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Transcript of Group 1.7 Denos, Khalid, Chen, Zhou, Peng 217, 991, 037, 337 , 641
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Group 1.7Denos, Khalid, Chen, Zhou, Peng
217, 991, 037, 337 , 641
Image from: https://www.tpg.com.au 1
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Outline
• Introduction
• System architecture
• System implementation
• Used cases
• Conclusion
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Internet Protocol Television (IPTV)
Image from: http://joannekraft.com
IP Network
Data Service
TV Service
Voice Service
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IPTV Monitoring
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STB SNMP Agent
Server
SNMPTrap
Server
STB SNMP Agent
• Collect data• Queue management• Filter and Parsing• Store
• Data Source• Periodical• Triggered by user (Channel Zapping)
Set Top Box
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System Architecture
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System Implementation
• Data Source (SNMP Traps)
Network Processing Network levelVideo decoding Application level
• Control using SNMP
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System Implementation
• Data Volume– 180 Bytes/msg, 100,000 subscribers
½ Min
4.8Mb/s
288 M msg 48 GB/day !!
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System Implementation
• Data Volume– 180 Bytes/msg, 100,000 subscribers
5 Minonly 5%
12 Kbps
1.44 M msg 370 MB/day !!
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System Implementation (server side)
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System Implementation (server side)
• Data Analysis– Diagram for historical data
– Diagram for real time data
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Database APP
Queries Data
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Use Cases
• Application-Level IPTV Quality Monitoring
• Integration with Customer Support
• Network Topology Mapping
• Error Localization
• Correlation with Weather Phenomena
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App-level IPTV Quality Monitoring
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Establish a baseline level of application-level metrics and network-related metrics
Detect any significant increase in errors = Experience of low quality
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Integration with Customer Support
Customer Customer Service Field Team
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Cost long time to describe the problems, and very likely in a wrong way!!
Usual Case
Customer Customer Service
Data collected
Field Team
Advantage:
1.Guide further decisions to mediate the problem
2.Shorten the delay between a decision and its results
After Integration
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Network Topology Mapping
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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.
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Error Localization
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Heat map of error severityvisualize 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 streaksA connection between independent BNGsOr a similar usage pattern
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Correlation With Weather Phenomena
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Natural causes
Lightning strikesCreate 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 MapExplain away the unavoidable and focus on the preventable
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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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