1. 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

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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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.

9. Analysis of Objects Small objects take up the least of the bandwidth However, most requests are for small objects

10. Analysis of Objects (continued..) Majority of requests are for small objects Majority of bytes transferred are due to the largest objects

11. 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

12. 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

13. Kazaa is not Zipf (continued..) The most popular objects are requested much less, while objects down the tail show elevated number of requests.

14. 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

15. 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)

16. 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

17. Any questions?