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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.
    • 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.