The document describes a query-cycle simulator for simulating peer-to-peer (P2P) networks. The simulator models properties of peers, including content, network parameters, and behavior over successive query cycles. Peer content is modeled by assigning files and interests according to observed distributions. Network parameters like topology and bandwidth are also modeled. Peer behavior, such as query generation and response, is simulated over query cycles based on models derived from measurements of real P2P systems. The simulator aims to accurately model key aspects of P2P networks to enable testing and comparison of P2P algorithms.

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

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

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

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

11. Content Type: Model Modeling Content Categories: Assume n content categories. C={c 1 ,c 2 ,…,c n } 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} Assign Files {c,r}={c1,f1} {c,r}={c1,f7} . . .

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

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

24. At each cycle, peer i may be: active inactive or down Query-Cycle Model 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 At each query cycle, probability of being up is drawn from distribution in Saroiu et al. Saroiu,Gummandi,and Gribble. A Measurement Study of Peer-to-Peer File Sharing Systems, 2002.

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

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