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The Query-Cycle Simulator for Simulating P2P Networks

The Query-Cycle Simulator for Simulating P2P Networks. Mario T. Schlosser Tyson E. Condie Sepandar D. Kamvar Stanford University. For each peer i { -Repeat until convergence { - Compute . . . - Send . . . } }. Problem. Problem:

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The Query-Cycle Simulator for Simulating P2P Networks

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  1. The Query-Cycle Simulator for Simulating P2P Networks Mario T. Schlosser Tyson E. Condie Sepandar D. Kamvar Stanford University

  2. For each peer i { -Repeat until convergence { -Compute. . . -Send . . . } } Problem • Problem: • Accurately Simulate Real-World P2P Networks. • Motivation: • Testing P2P Algorithms.

  3. Goals • P2P Simulator • Descriptive • Simple • Easily Extensible • Make it available on the web so that people can test and compare their algorithms on a standard platform.

  4. Query Cycle Model Query Cycle 1

  5. Query Cycle Model Query Cycle 2

  6. Query Cycle Model Query Cycle 3

  7. Properties to Model • Peer Content • Network Parameters • Peer Behavior

  8. Properties to Model • Peer Content • How Much? • What Type? • Network Parameters • Peer Behavior

  9. Data Volume • Observations • Model Saroiu,Gummandi,and Gribble. A Measurement Study of Peer-to-Peer File Sharing Systems, 2002. Simulator assigns # of files owned by peer i according to distribution.

  10. Content Type: Observations • Content Categories • Zipf distribution on file popularity Punk Rock Hip-Hop Jazz Crespo and Garcia-Molina. Semantic Overlay Networks, 2002. Korfhage, Information Storage and Retrieval, 1997.

  11. Content Type: Model • Modeling Content Categories: • Assume n content categories. C={c1,c2,…,cn} • A peer i is assigned content categories according to the Zipf distribution: • It is then assigned an interest level p(c|i) to each of the assigned content categories by a uniform random distribution.

  12. Content Type: Model • Modeling Files: • Each distinct file f may be uniquely identified by {c,r} • A peer is assigned files by:

  13. Recap on Content Assignment

  14. Recap on Content Assignment Assign Data Volume

  15. Recap on Content Assignment {c1, c3, c4} Assign Content Categories

  16. Recap on Content Assignment {c1=.5, c3=.3, c4=.2} Assign Interest Level to Content Categories

  17. Recap on Content Assignment {c1=.5, c3=.3, c4=.2} {c,r}={c1,f7} {c,r}={c1,f1} . . . Assign Files

  18. Properties to Model • Peer Content • Network Parameters • Topology • Bandwidth • Peer Behavior

  19. Network Parameters • Topology: • Observation: Power Law Topology • Model: probability of connecting to a peer is proportional to the degree of that peer. • Bandwidth • Simple Bandwidth Model • Can be easily extended.

  20. Properties to Model • Peer Content • Network Parameters • Peer Behavior

  21. Query-Cycle Model • At each cycle, peer i may be: • active • inactive • or down

  22. Query-Cycle Model • At each cycle, peer i may be: • active • inactive • or down • Issues a single query. • Waits for incoming responses. • Selects a source and downloads file. • Also: • Responds to queries. • Forwards query messages.

  23. Query-Cycle Model • At each cycle, peer i may be: • active • inactive • or down • Responds to queries. • Forwards Query Messages.

  24. Query-Cycle Model • At each cycle, peer i may be: • active • inactive • or down • Does nothing.

  25. Properties to Model • Peer Content • Network Parameters • Peer Behavior • Uptime and Session Duration • Query Activity • Queries • Query Responses • Downloads

  26. Uptime • Observations • Model Saroiu,Gummandi,and Gribble. A Measurement Study of Peer-to-Peer File Sharing Systems, 2002. At each query cycle, probability of being up is drawn from distribution in Saroiu et al.

  27. Queries • Observations • None • Model • Based on the idea that peers query for files in the same categories that they own.

  28. Responses and Downloads • Responses • If a peer receives a query for which it owns the file, it responds. • Source Selection • Random

  29. Extensions • Different Types of Peers • i.e., Malicious Peers • Different Models for Different Situations • Reputation-based source selection. • Edutella: model distribution over markups rather than content categories. • Web Services: Change models for content distribution, query activity, etc. However, parameters are the same.

  30. Samples

  31. Future Work • Test predictions against observations in P2P networks “in the wild”. • Observations, observations, observations. • Model other networks.

  32. The End • Code, demos will be available at http://www.stanford.edu/~sdkamvar/research.html next monday.

  33. Motivation Affected algorithms Network or peer property • Structuring algorithms • Whatever • Stability of trust algorithms • Topology • Content distribution • Bandwidth, uptime of peers

  34. Query Activity • Observations • Model Saroiu,Gummandi,and Gribble. A Measurement Study of Peer-to-Peer File Sharing Systems, 2002. At each query cycle, . . .

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