1 Analysis of Multimedia Analysis of Multimedia Workloads with Implications for Workloads with Implications for Internet Streaming Internet Streaming Lei Guo 1 , Songqing Chen 2 , Zhen Xiao 3 , and Xiaodong Zhang 1 Presented by: Zhen Xiao 1 College of William and Mary 2 George Mason University 3 AT&T Labs – Research The 14 th International World Wide Web Conference
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1 Analysis of Multimedia Workloads with Implications for Internet Streaming Lei Guo 1, Songqing Chen 2, Zhen Xiao 3, and Xiaodong Zhang 1 Presented by:
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Analysis of Multimedia Workloads with Analysis of Multimedia Workloads with Implications for Internet StreamingImplications for Internet Streaming
Lei Guo1, Songqing Chen2, Zhen Xiao3, and Xiaodong Zhang1
Presented by: Zhen Xiao
1College of William and Mary2George Mason University
3AT&T Labs – Research
The 14th International World Wide Web Conference
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Multimedia: Downloading
Web Server
Web Browser
MediaPlayer
HTTP
file
Long start-up latencyPotential waste of traffic
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Multimedia: Pseudo Streaming
Web Browser
MediaPlayer
Web Server
HTTP
also called progressive downloading or progressive playback
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Multimedia: Streaming
Web Server
Web Browser
MediaPlayer
HTTP
metafile
Streaming Server
RTSP/MMS/HTTP
RTP/RTCP
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Goals and Objectives
• How is multimedia content delivery doing in practice? – Streaming has many advantages over downloading for
multimedia traffic– But what percentage of multimedia traffic is delivered via
streaming?
• What are the implications of different content delivery methods for multimedia traffic?– bandwidth efficiency, playback quality, etc.– Can we quantify the actual benefits of a streaming service?
• What can we do to improve the current content delivery practice for multimedia traffic?
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Existing Work
• Streaming/Web sites– A small number of popular servers – No study on large number of Web sites
• Clients– Educational [USITS01][NOSSDAV01] or enterprise environments
[NOSSDAV02]– Very few study on commercial workloads
• Data Sources– Pre-stored video objects [MMCN98], server logs [NOSSDAV02]– No flow level information
• Focuses– Object popularity and sharing patterns, client interactivity
[WWW01][ICDCS05]– Few on content delivery methods
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Our Contributions
• Analyze two large commercial multimedia workloads– Much larger scale– More detailed information (e.g., byte counters)– Focus on multimedia delivery methods, bandwidth efficiency,
playback quality
• Design and simulation of the AutoStream system– Provide streaming service for standard Web servers– Share the cost of streaming service among content providers
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Outline
• Background
• Trace Collection and Processing
• Workload Analysis
• AutoStream
• Conclusions and future work
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Trace Collection
• Two packet level media workloads collected with the Gigascope appliance– Server Workload: a large number of commercial Web sties
hosted by a major ISP (a Web server farm)– Client Workload: a large group of home users connected to the
Internet via a well-known cable company (cable clients)– The two workloads are independent!– 24 hour duration: 06/15/2004 8pm – 06/16/2004 8pm
• We collected:– The first IP packets of all HTTP requests and responses– The first IP packets of all RTSP and MMS control messages– Byte counters: the number of bytes transferred through each
TCP/UDP connection per second– All HTTP based P2P traffic were carefully filtered out
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Traffic Overview
• Total data size: 100GB in gzip format• Server workload
– 1,095,984 media requests/response pairs– 4,498 unique server IPs, 79,309 unique client IPs
• Client workload– 579,693 media requests/response pairs– 13,110 unique server IPs, 7,906 unique client IPs
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Trace Processing
• Downloading– User-AgentUser-Agent in HTTP request is a Web browser– Content-TypeContent-Type in HTTP response is audioaudio or videovideo– application/multipartapplication/multipart: based on 34 most popular suffixes
for media files (e.g. .mp3.mp3, .mpeg.mpeg, etc.)
• Pseudo streaming– Subtle differences from downloading– User-AgentUser-Agent in HTTP request corresponds to a media player
• Most streaming uses RTSP and MMS– HTTP based streaming is very small
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Trace Processing (Cont’d)
• Processing– Decoded most popular media formats: Windows, Real, and
QuickTime– Extracted URL, media encoding rate, and playback time.– Requested traffic: Content-LengthContent-Length in HTTP response or
media length and encoding rate extracted from RTSP/MMS messages
– Transferred traffic: actually transferred data based on byte counters.
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Outline
• Background
• Trace Collection and Processing
• Workload Analysis
• AutoStream
• Conclusions and future work
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Multimedia Delivery Methods
Delivery Method Request Number Requested Traffic Transferred Traffic