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Measurements of Peer-to-Peer Systems

Measurements of Peer-to-Peer Systems. Pradnya Karbhari Nov 25 th , 2003 CS 8803: Network Measurements Seminar. Introduction to Peer-to-Peer (P2P) systems. End-systems (or peers), are capable of behaving as clients and servers of data, hence system is scalable and reliable

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Measurements of Peer-to-Peer Systems

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  1. Measurements ofPeer-to-Peer Systems Pradnya Karbhari Nov 25th, 2003 CS 8803: Network Measurements Seminar

  2. Introduction to Peer-to-Peer (P2P) systems • End-systems (or peers), are capable of behaving as clients and servers of data, hence system is scalable and reliable • Peers participation is voluntary, membership is dynamic, hence topology keeps changing • Most popularly used for file sharing, hence peer-to-peer systems have become synonymous with peer-to-peer file sharing networks

  3. Classification of P2P systems • P2P computation (e.g. seti@home) • P2P communication (instant messaging) • P2P file-sharing networks • Centralized (e.g. Napster) • Decentralized • Structured (e.g. Chord, CAN, Pastry, Tapestry) • Unstructured (e.g. Gnutella, Kazaa, Freenet, eDonkey, eMule, Direct Connect, …)

  4. Popularity of unstructured decentralized P2P networks • Gnutella host count, maintained by Limewire (http://www.limewire.com) • good scope for measurement studies because: • deployed and widely used • use a lot of bandwidth during data transfer, hence a concern for network operators • quite a few measurement studies have been done on these systems, some of which we will discuss in this seminar

  5. Outline • Characterization of users of P2P systems • Saroiu, et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002. • Effect of P2P traffic on the underlying network • Sen, et.al., “Analyzing peer-to-peer traffic across large networks”, IMW’02 • Peer-to-Peer Topologies • Ripeanu, et.al., “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design”, IEEE Internet Computing, 2002. • Searching on the P2P network • Sripanidkulchai, “The popularity of Gnutella queries and its implications on scalability”, 2001 • Deciphering proprietary P2P systems (like Kazaa) • Leibowitz, et.al., “Deconstructing the Kazaa Network”, WIAPP, 2003.

  6. Gnutella protocol overview • Connecting to the Gnutella network • bootstrap using GWebCache system and locally cached hostlist • Ping/Pong messages are exchanged with potential neighbors • Searching on the network • Query messages are flooded on the network • QueryHit messages are received (back-propagated along Query path) from peers having the requested content • Downloading the content • peers download files directly from peers having the requested content

  7. Characterization of Users of P2P systems S. Saroiu, P. Gummadi and S. Gribble, “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN’02. • first paper to characterize p2p file sharing systems • Goal: To analyze the following user characteristics • latency • lifetime of peers • bottleneck bandwidth • number of files shared and downloaded • degree of cooperation • methodology: active crawling • systems studied: Napster and Gnutella • data collection: May 2001

  8. Measurement Methodology • active crawling of the Napster and Gnutella systems • Napster: issued queries for popular content, and then queried central server for peer information • Gnutella: used ping/pong messages in protocol to get metadata about peers, and then their neighbors and so on • parallel measurement for: • peer lifetime- periodic probing of peers obtained from crawlers • offline if no response to TCP SYN • inactive if response to TCP SYN is a TCP RST • active if accepts the incoming TCP connection on that port • latency- RTT measurements from one host • bottleneck link bandwidth- active probing using Sprobe, a tool they developed based on packet-pair dispersion technique Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  9. Host Lifetime analysis • 20% peers in Napster, Gnutella have IP-level uptime of 93% or more • Napster peers have higher application uptimes than Gnutella peers • the best 20% of Napster peers have uptime of 83% or more and the best 20% of Gnutella peers have uptime of 45% or more • median session duration is 60 minutes for Napster and Gnutella Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  10. Latency analysis (Gnutella) • 20% peers have a latency of at most 70ms and 20% have a latency of at least 280ms • correlation between downstream bottleneck bandwidth and latency: two clusters for modems (20-60Kbps, 100-1000ms) and broadband (1Mbps, 60-300ms) Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  11. Bottleneck Bandwidth Analysis (Gnutella) • 92% Gnutella peers have downstream bottleneck bandwidth of at least 100Kbps • 22% peers have upstream bottleneck bandwidth of 100Kbps or less • peers are unsuitable to serve content Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  12. Downloads, Uploads and Shared Files • relative number of downloads and uploads varies significantly across bandwidth classes • clear client/server behavior of different classes Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  13. Shared files v/s Shared Data(Napster and Gnutella) • Strong correlation between number of files shared and amount of shared MB of data • slope of both lines is 3.7MB, the size of a typical MP3 audio file Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  14. Degree of Cooperation (Napster) • 30% of the peers report bandwidth as 64Kbps or less, but actually have significantly higher bandwidths • 10% of the peers reporting higher bandwidths (3Mbps or higher) actually have significantly lower bandwidth Saroiu et.al., “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN, 2002

  15. Effect of P2P traffic on underlying network S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW 2002. • Goal: To characterize p2p traffic at three aggregation levels- IP, prefix and AS • host distribution and host connectivity • traffic volume and mean bandwidth usage • traffic patterns over time • connection duration and on-time methodology: passive measurements at routers (port based) • systems studied: FastTrack(Kazaa), Gnutella, Direct Connect • analysis of flow-level data collected from multiple border routers across a large tier-1 ISP’s backbone

  16. Measurement Methodology • flow records from multiple border routers matching ports: • 6346/6347: Kazaa • 1214: FastTrack • 411/412: Direct Connect • processed data to eliminate • private IP addresses • invalid AS numbers • final data set contained 800 million flow records S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  17. Datasets used for analysis • FastTrack is most popular in terms of number of hosts participating and average traffic volume per day • rapid growth of P2P traffic is mainly caused by increasing number of hosts in the system • Direct Connect systems have higher traffic volume per IP address S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  18. Host distribution analysis • # of IP addresses in FastTrack ranges from 0.5 to 2 million • ratio of # of IP addresses in FastTrack:Gnutella:DirectConnect is 150:30:1 • Density of a prefix is the number of unique active IP addresses belonging to it • Density of an AS is the number of unique prefixes belonging to it • FastTrack hosts are distributed more densely than Gnutella and Direct Connect hosts (64:16:4) S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  19. Host connectivity analysis (FastTrack) • 48% of individual IPs communicate with at most one IP and 89% with at most 10 IPs • 75% of prefixes and ASes communicate with at least 2 prefixes or ASes • very few hosts have very high connectivity and most hosts have very low connectivity S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  20. Traffic volume analysis • CDF of traffic volume per IP/prefix/AS for FastTrack (one day) • distribution of P2P upstream traffic volume across three months S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  21. Mean bandwidth usage(FastTrack and Direct Connect) • FastTrack: 33% IP addresses have mean downstream b/w 56Kbps or less; 50% have mean upstream b/w 56Kbps or less • Direct Connect: 20% IP addresses have mean downstream b/w 56Kbps or less; 33% have mean upstream b/w 56Kbps or less S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  22. Traffic patterns over time (FastTrack) • traffic volume transferred every hour among FastTrack hosts • number of unique IP addresses, prefixes, ASes active every hour • number of active unique IP addresses in each bin of various sizes • system is very dynamic- hosts join and leave frequently S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  23. Connection duration and On-time (FastTrack) • 50% of the IPs are online for less than one minute/day • 60% IPs, 40% prefixes, 30% ASes stay for less than 10 mins/day • 65% of the IPs join only once • AS, prefix level- not very transient S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW, 2002

  24. Peer-to-Peer Topologies M. Ripeanu, I. Foster and A. Iamnitchi, “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design”, IEEE Internet Computing Journal, 2002. • Goal: To discover and analyze the Gnutella overlay topology and evaluate generated traffic • methodology: active crawling • datasets: Nov 2000, March 2001 and May 2001

  25. Gnutella Network Growth • number of nodes in the largest connected component in the Gnutella network • significantly larger network found during Memorial Day and Thanksgiving • 50 times increase within 6 months Ripeanu, et.al., “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems”, 2002

  26. Distribution of node-to-node shortest paths • more than 95% node pairs are at most 7 hops away • longest node-to-node path is 12 hops Ripeanu, et.al., “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems”, 2002

  27. Averag node connectivity • average number of connections per node remains constant = 3.4 Ripeanu, et.al., “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems”, 2002

  28. Node connectivity distribution • Nov 2000: Gnutella nodes organize themselves in a power law • March 2001: connectivity does not look like a power law for all nodes; power law distribution is preserved for nodes with more than 10 links; for less than 10 links, the distribution is almost constant Ripeanu, et.al., “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems”, 2002

  29. Searching on the P2P network K. Sripanidkulchai, “The popularity of Gnutella queries and its implications on scalability”, 2001, http://www-2.cs.cmu.edu/~kunwadee/research/p2p/gnutella.html • methodology: passive measurements at one or two peers, made part of the Gnutella network, to log queries and query messages routed through it • data sets: Dec 2000, Jan 2001

  30. Top 20 most popular query types • 17% queries contained non-ASCII strings- filtered them out • most queries for artists, adult content and file extensions (audio) • some queries for books, software etc. K. Sripanidkulchai, “The popularity of Gnutella queries and its implications on scalability”, 2001.

  31. Query popularity distribution • two distinct distributions of document popularity, with a break at query rank 100 • most popular documents are equally popular • less popular documents follow a Zipf-like distribution, with alpha beween 0.63 and 1.24 K. Sripanidkulchai, “The popularity of Gnutella queries and its implications on scalability”, 2001.

  32. Deciphering proprietary P2P systems Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003. • methodology: passive content-based data collection at a caching server installed at the border of a large ISP • L4 switch inspects first few packets of each TCP connection to detect Kazaa download traffic • redirects Kazaa download traffic through caching server • focus on download traffic only, not control traffic (since it is encrypted)

  33. Characteristics of Collected Traces • 38% of all download sessions do not use standard Kazaa port (1214) Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003

  34. File download distribution by bytes • CDF of byte popularity distribution for 10%, 1% most popular files • 0.8 % of all files account for 80% of the generated traffic • 0.1% of the most bandwidth hungry files (top 1% of all files) generate 50% traffic Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003

  35. File size distribution • note the log-scale on X-axis • 3 distinct modes • 100KB for pictures • 2-5MB for music files • 700MB for movies Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003

  36. Quantity and Rate of Distinct Files • new files seen at different time scales- every day, hour, minute • 150,000 distinct files during a 17-day period • daily graph: new files seen continued to decrease, but no steady state value (rate of injection of files in the network) achieved • hourly graph: time of day effect • per-minute graph: 50 new files seen every minute on an average Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003

  37. Rate of change of popularity of files • percentage of files that make it to the N most popular files list- (a) in consecutive intervals and (b) after T intervals, compared with first list • measurement interval is 24 hours • 15% of the highly popular files remain popular throughout the experiment, and the rest are popular at short time intervals Leibowitz, M. Ripeanu and A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP, 2003

  38. Open Questions • Mapping a global snapshot of the entire Gnutella topology • Bootstrapping of peers in unstructured peer-to-peer systems (work in progress) • More efficient searching on P2P networks- efforts in this direction include random walks, bloom-filter based techniques etc. • End-point privacy/anonymity is absent in most of these peer-to-peer networks

  39. References • Papers covered in the seminar: • S. Saroiu, P. Gummadi and S. Gribble, “A Measurement Study of Peer-to-Peer File Sharing Systems”, MMCN 2002. • S. Sen and J. Wang, “Analyzing peer-to-peer traffic across large networks”, IMW 2002. • M. Ripeanu, I. Foster, A. Iamnitchi, “Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design”, IEEE Internet Computing, 2002. • Sripanidkulchai, “The popularity of Gnutella queries and its implications on scalability”, 2001. • N. Leibowitz, M. Ripeanu, A. Wierzbicki, “Deconstructing the Kazaa Network”, WIAPP 2003. • Papers not covered in the seminar: • J. Chu, K.Labonte and B. Levine, “Availability and Locality Measurements of Peer-to-Peer File Systems”, SPIE, July 2002. • F. Bustamante and Y. Qiao, “Friendships that last: Peer lifespan and its role in P2P protocols”, WCW 2003. • R. Bhagwan, S. Savage and G. Voelker, “Understanding Availability”, IPTPS 2003. • Saroiu, et.al., “An Analysis of Internet Content Delivery Systems”, OSDI 2002. • Markatos et.al., “Tracing a large-scale Peer-to-Peer System: An hour in the life of Gnutella”, CCGrid 2002.

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