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YouTube Traffic Characterization: A View From the Edge. Phillipa Gill¹ , Martin Arlitt²¹, Zongpeng Li¹, Anirban Mahanti³ ¹ Dept. of Computer Science, University of Calgary, Canada ² Enterprise Systems & Software Lab, HP Labs, USA ³ Dept. of Computer Science and Engineering, IIT Delhi, India.

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youtube traffic characterization a view from the edge

YouTube Traffic Characterization: A View From the Edge

Phillipa Gill¹, Martin Arlitt²¹,

Zongpeng Li¹, Anirban Mahanti³

¹Dept. of Computer Science, University of Calgary, Canada

²Enterprise Systems & Software Lab, HP Labs, USA

³Dept. of Computer Science and Engineering, IIT Delhi, India

introduction
Introduction
  • The way people use the Web is changing.
  • Creation and sharing of media:
    • Fast, easy, cheap!
  • Volume of data associated with extremely popular online media.
what is web 2 0
What is Web 2.0?
  • User generated content
    • Text: Wordpress, Blogspot
    • Photos: Flickr, Facebook
    • Video: YouTube, MySpace
  • Social Networking
    • Facebook, MySpace
  • Tagging
    • Flickr, YouTube
youtube facts and figures
YouTube: Facts and Figures
  • Founded in February 2005
    • Enabled users to easily share movies by converting them to Flash
  • Largest video sharing Website on the Internet [Alexa2007]
  • Sold to Google for $1.65 billion in November 2006
how youtube works 1 2
How YouTube Works (1/2)

GET: /watch?v=wQVEPFzkhaM

OK (text/html)

GET: /vi/fNaYQ4kM4FE/2.jpg

OK (img/jpeg)

how youtube works 2 2
How YouTube Works (2/2)

GET: swfobject.js

OK (application/x-javascript)

GET: /p.swf

OK (application/shockwave-flash)

GET: /get_video?video_id=wQVEPFzkhaM

OK (video/flv)

our contributions
Our Contributions
  • Efficient measurement framework
  • One of the first extensive characterizations of Web 2.0 traffic
    • File properties
    • File access patterns
    • Transfer properties
  • Implications for network and content providers
outline
Outline
  • Introduction & Background
  • Contributions
  • Methodology
  • Results
  • Implications
  • Conclusions
our view points
Our View Points
  • Edge (University Campus)
    • 28,000 students
    • 5,300 faculty & staff
    • /16 address space
    • 300Mb/s full-duplex network link
  • Global
    • Most popular videos
campus data collection
Campus Data Collection
  • Goals:
    • Collect data on all campus YouTube usage
    • Gather data for an extended period of time
    • Protect user privacy
  • Challenges:
    • YouTube’s popularity
    • Monitor limitations
    • Volume of campus Internet usage
our methodology
Our Methodology
  • Identify servers providing YouTube content
  • Use bro to summarize each HTTP transaction in real time
  • Restart bro daily and compress the daily log
  • Map visitor identifier to a unique ID
categories of transactions
Categories of Transactions
  • Complete – the entire transaction was parsed successfully
  • Interrupted – TCP connection was reset
  • Gap – monitor missed a packet
  • Failure – transaction could not be parsed
global data collection
Global Data Collection
  • Crawling all videos is infeasible
  • Focus on top 100 most popular videos
    • Four time frames: daily, weekly, monthly and all time.
  • 2 step data collection:
    • Retrieve pages of most popular videos
    • Use YouTube API to get details on these videos
outline17
Outline
  • Introduction & Background
  • Contributions
  • Methodology
  • Results
  • Implications
  • Conclusions
results
Results
  • Campus Usage Patterns
  • File Properties
  • File Access Patterns
  • Transfer Properties
results20
Results
  • Campus Usage Patterns
  • File Properties
  • File Access Patterns
  • Transfer Properties
unique file sizes
Unique File Sizes
  • Video data is significantly larger than the other content types
time since modification
Time Since Modification
  • Videos and images rarely modified
  • Text and application data modified more frequently
video durations
Video Durations
  • Spike around 3 minutes likely music videos
  • Campus videos are relatively short: μ=3.3 min
summary of file properties
Summary of File Properties
  • Video content is much larger than other content types
  • Image and video content is more static than application and text content
  • Video durations are relatively short

Videos viewed on campus tend to be more than 1 month old

results25
Results
  • Campus Usage Patterns
  • File Properties
  • File Access Patterns
  • Transfer Properties
relative popularity of videos
Relative Popularity of Videos
  • Video popularity follows a weak Zipf distribution
  • Possibly due to edge network point of view

β = 0.56

commonality of videos
Commonality of Videos
  • ~10% commonality between consecutive days during the week
  • ~5% commonality between consecutive days on the weekend
summary of file referencing
Summary of File Referencing
  • Zipf distribution is weak when observed from the edge of the network
  • There is some overlap between videos viewed on consecutive days
  • Significant amount of content viewed on campus is non-unique
results29
Results
  • Campus Usage Patterns
  • File Properties
  • File Access Patterns
  • Transfer Properties
transfer sizes
Transfer Sizes

Flash player (p.swf, player2.swf)

Javascripts

transfer durations
Transfer Durations
  • Video transfers have significantly longer durations than other content types
summary of transfer properties
Summary of Transfer Properties
  • Javascript and flash objects have an impact on the size of files transferred
  • Video transfers have significantly larger sizes and durations
outline33
Outline
  • Introduction & Background
  • Contributions
  • Methodology
  • Results
  • Implications
  • Conclusions
implications for network providers
Implications for Network Providers
  • Web 2.0 poses challenges to caching
    • Larger multimedia files
    • More diversity in content
  • Meta data may be used to improve caching efficiency
implications for content providers
Implications for Content Providers
  • Multimedia content is large!
  • 65,000 videos/day x 10MB/video = 19.5 TB/month
  • Long tail effect -> much of the content will be unpopular
    • Cheap storage solutions
  • Longer transfer durations for video files
    • more CPU cycles required for transfers
conclusions
Conclusions
  • Multimedia content has much larger transfer sizes and durations than other content types
  • From the edge of the network, video popularity follows a weak Zipf distribution
  • Web 2.0 facilitates diversity in content which poses challenges to caching
  • New approaches are needed to efficiently handle the resource demands of Web 2.0 sites
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