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Streaming Video Traffic: Characterization and Network Impact

Streaming Video Traffic: Characterization and Network Impact. Kobus van der Merwe Shubho Sen Chuck Kalmanek {kobus,sen,crk}@research.att.com. Streaming Media Study: Why ?. Lot of streaming on the Internet Quality is getting pretty good Streaming is not well understood User behavior

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Streaming Video Traffic: Characterization and Network Impact

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  1. Streaming Video Traffic: Characterization and Network Impact Kobus van der Merwe Shubho Sen Chuck Kalmanek {kobus,sen,crk}@research.att.com

  2. 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

  3. On Demand Dates: 12/2001-03/2002 # Requests: 3.5 million # Unique IPs : 0.5 million # Unique ASs : 6600 WM and Real BW:~56 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 The Data On Demand: prerecorded clips from current affairs & information site Live: commerce oriented continuous live stream Traffic volume : several terabytes Routing data: daily BGP table dumps from Tier-1 ISP

  4. 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 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 By protocol (WM/Real): Windows Media dominates: 77% requests, 76% bytes

  5. 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

  6. On Demand: per-AS breakdown by stream bandwidth # Requests Traffic volume Most ASs generate more High Bandwidth traffic

  7. 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

  8. On Demand: Network Traffic Distribution # Requests Volume Significant variability in traffic contributions: 10% ASes account for 82% requests, 85% data

  9. 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

  10. 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

  11. On Demand :Traffic Time Series Significant variability within/across days Peak = 31 * Mean

  12. On Demand :Rapid Increase in Load Load increases 57 times in 10 minutes !

  13. Live: Traffic Time Series Significant variability within/across days Peak = 9* Mean

  14. 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

  15. 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

  16. Summary • Windows Media dominates • High encoding rates dominate • TCP transport dominate • Highly skewed request + volume distributions • Tier-1 ISPs cover %% < 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

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