Measurement, Modeling, and Analysis of a PeertoPeer File sharing Workload

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Measurement, Modeling, and Analysis of a
Peer-to-Peer File sharing Workload

• Krishna P. Gummadi, Richard J. Dunn, Stefan
  Saroiu, Steven D. Gribble, Henry M. Levy, John
  Zahorjan

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Outline

• Motivation
• Goals
• Approach
• Analysis of Users
• Analysis of Objects
• Kazaa is not Zipf
• Exploiting Locality
• Conclusion

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Motivation

• Dramatic shift of Internet traffic from WWW to
  multimedia file sharing
• March 2000 study found that bandwidth consumed by
  Napster was greater than HTTP
• On the UDUB campus, peer-to-peer file sharing
  consumed 43, WWW traffic 14
• Multimedia file sharing dominates now, and will
  dominate Internet of the future

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Goals

• To understand the fundamental properties of
  multimedia file-sharing systems
• To explore the forces driving P2P file-sharing
  workloads
• To demonstrate that opportunity exists to
  optimize performance in current file-sharing
  workloads

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Approach

• Analyze a 200-day trace of Kazaa traffic at the
  University of Washington
• Over 60,000 faculty, students, and staff
• 20 TBs of incoming data (1.6 million requests)
• Long enough to observe seasonal variations
• Derive a model of this multimedia traffic
• Use simulation to quantify the potential to
  improve performance of file-sharing

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Analysis of Users

• Kazaa users are patient
• In the WWW, users expect instant results
• The Web is an interactive system, whereas Kazaa
  is a batch-mode delivery system

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Analysis of Users (continued..)

• Users slow down as they age
• Older clients consume fewer bytes than newer
  clients
• Due to attrition (clients leaving the system
  forever) and older clients having slower request
  rates

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User Summary

• New clients generate most of the load in Kazaa
• Older clients consume fewer bytes as they age
• This is because of attrition clients leave the
  system permanently as they grow older.
• Older clients also tend to interact with the
  system at a constant rate, but ask for less
  during each interaction.

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Analysis of Objects

• Small objects take up the least of the bandwidth
• However, most requests are for small objects

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Analysis of Objects (continued..)

• Majority of requests are for small objects
• Majority of bytes transferred are due to the
  largest objects

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Analysis of Objects (continued..)

• Crucial difference (Web/multimedia)
• Multimedia objects are immutable
• Kazaa clients fetch objects at most once
• 94 an object is requested at most once
• Popularity of Kazaa objects is often short-lived
• Most popular objects tend to be recently born
• Most requests are for old objects
• Large objects requested tend to be older than
  small objects

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Kazaa is not Zipf

• Zipfs law popularity of ith-most popular object
  is proportional to i-a
• Distribution looks linear when plotted on a
  log-log scale

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Kazaa is not Zipf (continued..)

• The most popular objects are requested much less,
  while objects down the tail show elevated number
  of requests.

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Exploiting Locality

• Exploitation of locality in file-sharing
• To decrease external bandwidth usage
• There is a tremendous amount of untapped locality
  in the Kazaa workload
• Used a proxy cache at the organizational border,
  guaranteeing that every object is downloaded into
  the organization at most once
• Additional requests satisfied without consuming
  external bandwidth

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Exploiting Locality (continued..)

• 68 byte hit rate for large objects (22.3 TB
  saved)
• 37 byte hit rate for small objects (1.5 TB saved)

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Conclusion

• Client/object births drive P2P file-sharing
• Changes to objects drive the Web
• Fetch-at-most-once causes distribution of objects
  to deviate substantially from Zipf
• There is significant locality in Kazaa
• Opportunity for caching to reduce wide-area
  bandwidth consumption

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Any questions?