An Analysis of Internet Content Delivery Systems

An Analysis of Internet Content Delivery Systems (2002)

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

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Datum	Value	Source
TITLE	An Analysis of Internet Content Delivery Systems	INFERENCE
AUTHOR NAME	Stefan Saroiu	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR NAME	Krishna P. Gummadi	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR NAME	Richard J. Dunn	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR NAME	Steven D. Gribble	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR NAME	Henry M. Levy	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
ABSTRACT	In the span of only a few years, the Internet has experienced an astronomical increase in the use of specialized content delivery systems, such as content delivery networks and peer-to-peer file sharing systems. Therefore, an understanding of content delivery on the Internet now requires a detailed understanding of how these systems are used in practice. This paper examines content delivery from the point of view of four content delivery systems: HTTP web traffic, the Akamai content delivery network, and Kazaa and Gnutella peer-to-peer file sharing traffic. We collected a trace of all incoming and outgoing network traffic at the University of Washington, a large university with over 60,000 students, faculty, and staff. From this trace, we isolated and characterized traffic belonging to each of these four delivery classes. Our results (1) quantify the rapidly increasing importance of new content delivery systems, particularly peerto-peer networks, (2) characterize the behavior of these systems from the perspectives of clients, objects, and servers, and (3) derive implications for caching in these systems. 1	SVM HeaderParse 0.2
YEAR	2002	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	33 found	ParsCit 1.0