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

Measurement, Modeling, and Analysis of a Peer-to-Peer File sharing WorkloadKrishna P. Gummadi, Richard J. Dunn, Stefan Saroiu, Steven D. Gribble, Henry M. Levy, John Zahorjan

Outline

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

Motivation

Dramatic shift of Internet traffic from WWW to multimedia file sharingMarch 2000 study found that bandwidth

consumed by Napster was greater than HTTPOn 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

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

Approach

Analyze a 200-day trace of Kazaa traffic at the University of WashingtonOver 60,000 faculty, students, and staff20 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

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

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

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.

Analysis of Objects

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

Analysis of Objects (continued..)

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

objects

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

Kazaa is not Zipf

Zipf’s law: popularity of ith-most popular object is proportional to i- Distribution looks linear when plotted on a log-log scale

Kazaa is not Zipf (continued..)

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

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

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)

Conclusion

Client/object births drive P2P file-sharingChanges to objects drive the Web

Fetch-at-most-once causes distribution of objects to deviate substantially from Zipf

There is significant locality in KazaaOpportunity for caching to reduce wide-area

bandwidth consumption

Any questions?