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Characterizing Files in the Modern Gnutella Network:

A Measurement Study

Shanyu Zhao, Daniel Stutzbach, Reza Rejaie

University of Oregon

SPIE Multimedia Computing and Networking 2006 (MMCN’06), 18-19th January 2006San Jose, California, USA

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Outlines

Measurement study of modern Gnutella system

Conduct static, topological and dynamic analysis

Help to improve design and evaluations of P2P file-sharing applications

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Previous studies

Focus on a small population Be more than three years old Not examine dynamics of file characteristics

over time and correlation between the overlay topology and file distribution

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Why Gnutella

Top three (eDonkey2K, FastTrack, Gnutella) Gnutella has Browse-Host extension to extra

ct the list of shared files from peers One of most studied P2P systems; compare

and contrast with previous studies

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Original Gnutella

A new node joins the system (Node A) Node A connects to some node (Node B) by pre-

existing list, a particular website, IRC and etc Node B sends its working nodes to Node A Node A connects provided nodes till certain

threshold During search, Node A sends requests to connected

nodes which in turn forward requests

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Original Gnutella

Nodes reply the request directly or indirectly depending on the firewall existence

Node A downloads file pieces from one ore more positive nodes

Unlike Napster, Gnutella is decentralized; flood-based searches

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Modern Gnutella

Contrast to unstructured overlay topology, most modern Gnutella clients adopt a two-tier overlay structure

Ultrapeers and leaf peers (majority) Legacy peers (not implement ultrapeer featur

e)

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Measurement methodology

Problems of general crawlers Slow, distorted, inflate population

Previous studies Partial snapshot, periodic probe of a fixed group Significance is doubted

Goal of this work Capture entire population (?) Short period

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Measurement methodology

Topology crawl List of neighboring nodes

Content crawl List of available files of each node Need more

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Cruiser

Parallel P2P crawler Orders of magnitude faster than previous

crawlers (?) Master-slave architecture

Slave crawls hundreds of peers and master coordinates multiple slaves

Increase degree of concurrency

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Cruiser

Using 6 off-the-shelf 1GHz GNU/Linux boxes, crawl takes 15min + 5.5hr + 15min ~ 6 hours

Each content crawl takes 10GB log file containing file name and content hash

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Dataset

Three measurement periods; within each period, take snapshots everyday

6/8/2005-6/18/2005, 8/23/2005-9/9/2005 and 10/11/2005-10/21/2005

Examine both short and long timescales

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Dataset

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Sources of unreachable nodes

Firewall Severe network congestion Peer departed Not support Browse Host protocol

Ultrapeers: depart Leaf peers: depart and firewall Contact 20% peers (~half a million)

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Problems

Low-bandwidth TCP connection Some crawls do not complete after the timeout threshold,

as they are sent at extremely low rate

File identity File name is not a reliable file identifier; so this work use

content hash

Post-processing More than 100 million distinct files Divide into 7 segments randomly, trim files of less than 10

copies in a segment, combine trimmed back to one

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Static analysis

Ratio of free riders Degree of resources sharing among

cooperative peers File popularity distribution File type analysis

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Ratio of free riders

Free riders drop, ratio of ultrapeers is lower, long-lived peers slightly higher, # files not strongly correlate

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Degree of resources sharing among cooperative peers

Distribution of # peers sharing x files – power-law distribution

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Degree of resources sharing among cooperative peers

Distribution of contributed disk space – power-law distribution

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Degree of resources sharing among cooperative peers

Correlation not as strong as previous studies Discernable line with slope 3.7MB/file which

is typical size of MP3 audio file

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File popularity distribution

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File type analysis

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File type analysis

Previous studies Current studies

Music 67.2% files

79.2% bytes

67% files

40% bytes

Video 2.1% files

19.1% bytes

6% files

52.5% bytes

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Topological analysis

Per-file perspective – figure a & b Per-peer perspective – figure c

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Topological analysis

Churn (dynamics of peer participation) is dominant factor Depart Join Leaf peers become ultrapeers Rapid change in overlay topology prevents format

ion of topological clustering

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Dynamics analysis

Variations in shared files by individual peers Variations in popularity of individual files Trends in popularity variations

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Variations in shared files by individual peers

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Variations in popularity of individual files

Focus on top 100 and top 1000 files

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Trends in popularity variations

Track top 10 files across several days (fig a & b) Over several months (fig c)

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Conclusion

Use parallel crawl to obtain snapshots of peer connectivity and available files

Conduct three types of analysis Understand the distribution, correlation and

dynamics of available files

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Summary of findings

Free riding significantly drops # shared files and contributed storage space

by individual peers follow power-law distribution most peers contribute little disk space (<100MB) while small # peers contribute very large space (50-100GB)

Popularity of individual files follow Zipf distribution small # files are extremely popular but majority of files are very unpopular

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Summary of findings

Most popular file type is MP3 file (2/3 of all files, 1/3 of all bytes)

Popularity and occupied space by video files has tripled over past few years

# video files < 1/10 of audio files but occupy 25% more bytes

93% of bytes or 73% of files are multimedia files

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Summary of findings

Files are randomly distributed; no strong correlation between the available files at peers that are one, two or three hops apart in overlay topology

Shared files by individual slowly change over timescale of days; more popular files experience larger variations in popularity