Group 1.7Denos, Khalid, Chen, Zhou, Peng

217, 991, 037, 337 , 641

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Outline

• Introduction

• System architecture

• System implementation

• Used cases

• Conclusion

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Internet Protocol Television (IPTV)

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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

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

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

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

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.

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

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

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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