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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

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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

  • 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?

  • User generated content

    • Text: Wordpress, Blogspot

    • Photos: Flickr, Facebook

    • Video: YouTube, MySpace

  • Social Networking

    • Facebook, MySpace

  • Tagging

    • Flickr, YouTube


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)

GET: /watch?v=wQVEPFzkhaM

OK (text/html)

GET: /vi/fNaYQ4kM4FE/2.jpg

OK (img/jpeg)


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

  • 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

  • Introduction & Background

  • Contributions

  • Methodology

  • Results

  • Implications

  • Conclusions


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

  • 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

  • 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

  • Complete – the entire transaction was parsed successfully

  • Interrupted – TCP connection was reset

  • Gap – monitor missed a packet

  • Failure – transaction could not be parsed


Categories of Transactions (2)


Our Traces


HTTP Response Codes


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


Outline

  • Introduction & Background

  • Contributions

  • Methodology

  • Results

  • Implications

  • Conclusions


Results

  • Campus Usage Patterns

  • File Properties

  • File Access Patterns

  • Transfer Properties


Campus Usage Patterns

Reading

Break


Results

  • Campus Usage Patterns

  • File Properties

  • File Access Patterns

  • Transfer Properties


Unique File Sizes

  • Video data is significantly larger than the other content types


Time Since Modification

  • Videos and images rarely modified

  • Text and application data modified more frequently


Video Durations

  • Spike around 3 minutes likely music videos

  • Campus videos are relatively short: μ=3.3 min


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


Results

  • Campus Usage Patterns

  • File Properties

  • File Access Patterns

  • Transfer Properties


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

  • ~10% commonality between consecutive days during the week

  • ~5% commonality between consecutive days on the weekend


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


Results

  • Campus Usage Patterns

  • File Properties

  • File Access Patterns

  • Transfer Properties


Transfer Sizes

Flash player (p.swf, player2.swf)

Javascripts


Transfer Durations

  • Video transfers have significantly longer durations than other content types


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


Outline

  • Introduction & Background

  • Contributions

  • Methodology

  • Results

  • Implications

  • Conclusions


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

  • 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

  • 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


Questions?

Contact

psessini@ucalgary.ca


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