measurement, modeling, and analysis of a peer-to-peer file sharing workload
DESCRIPTION
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. Outline. Motivation Goals Approach Analysis of Users Analysis of Objects Kazaa is not Zipf Exploiting Locality - PowerPoint PPT PresentationTRANSCRIPT
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