Streaming Video Traffic: Characterization and Network Impact

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Streaming Video Traffic Characterization and
Network Impact

• Kobus van der Merwe
• Shubho Sen
• Chuck Kalmanek
• kobus,sen,crk_at_research.att.com

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Streaming Media Study Why ?

• Lot of streaming on the Internet
• Quality is getting pretty good
• Streaming is not well understood
• User behavior
• Factors that impact quality
• Network impact distribution
• Reasons
• Proprietary protocols
• WM, Real
• Very commercial
• logs files are sensitive and hard to get

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The Data
On Demand prerecorded clips from current affairs
information site Live commerce oriented
continuous live stream

• On Demand
• Dates 12/2001-03/2002
• Requests 3.5 million
• Unique IPs 0.5 million
• Unique ASs 6600
• WM and Real
• BW56 Kbps and 300 Kbps

• Live
• Dates 02/2002 03/2002
• Requests 1 million
• Unique IPs 0.28 million
• Unique ASs 4000
• WM only
• BW 100 Kbps encoded

Traffic volume several terabytes
Routing data daily BGP table dumps from Tier-1
ISP
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On Demand Traffic Composition

• By Bandwidth (56 Kbps/300 Kbps)
• High BW dominates 65 requests, 95 bytes
• Low BW 35 of sessions account for just 5 of
  data

By protocol (WM/Real) Windows Media dominates
77 requests, 76 bytes

• By transport
• HTTP 37 requests, 27 bytes
• TCP 29 requests, 45 bytes
• UDP 34 requests, 28 bytes
• Proprietary Streaming dominates 63 requests,
  73 bytes
• Total TCP dominates 66 requests, 72 bytes
• - probably because of firewalls

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On Demand per-AS breakdown by protocol
ASs contributing 80 requests or 80 traffic
Traffic volume
Requests
Most ASs generate more MMS than RealMedia
Traffic
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On Demand per-AS breakdown by stream bandwidth
Requests
Traffic volume
Most ASs generate more High Bandwidth traffic
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Live Traffic Composition

• By transport
• HTTP 55 requests, 47 bytes
• TCP 17 requests, 38 bytes
• UDP 28 requests, 17 bytes
• Proprietary Streaming (TCP UDP) 45
  requests, 55 bytes
• Total TCP dominates 72 requests, 85 bytes
• - probably because of firewalls
• Proprietary Streaming, HTTP have similar shares

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On Demand Network Traffic Distribution
Requests
Volume
Significant variability in traffic
contributions 10 ASes account for 82 requests,
85 data
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Content Distribution Impact

• Goal Evaluate different content distribution
  approaches
• Centralized IP peering
• Centralized content peering
• Centralized replica placement
• Assume traffic distributed from (originating
  from) Tier-1 ISP
• Look at coverage achieved by different approaches
  
• Traffic distribution using AS hop-count from
  Tier-1 ISP as a metric
• Assumption for streams originating in Tier-1 ISP
  small AS-hop count will increase probability of
  acceptable quality

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Content Distribution Impact

• Selected ASes consistent contributors out of
  6600
• Caveats
• Hop count not good metric of anything
• Limited data set
• Data set might be self selecting

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On Demand Traffic Time Series
Significant variability within/across days Peak
31 Mean
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On Demand Rapid Increase in Load
Load increases 57 times in 10 minutes !
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Live Traffic Time Series
Significant variability within/across days Peak
9 Mean
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Object Popularities
Volume 320 clips
Sessions 320 clips
Few heavy-hitters account for bulk of traffic Dec
13 top 5 clips account for 85 of traffic
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On Demand Session Characteristics
High Bw mms
Low Bw mms
Most sessions download a fraction of the
object. A larger proportion of high bw clip is
downloaded
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Summary

• Windows Media dominates
• High encoding rates dominate
• TCP transport dominate
• Highly skewed request volume distributions
• Tier-1 ISPs cover lt 2 AS hops
• Significant coverage with small selective
  arrangements
• High variability in daily traffic patterns
• Ramp up in tens of minutes
• Highly skewed object popularity
• High bit-rate clips watched longer