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Optimisation des DHT à partir des propriétés physiques, logiques et sociologiques des clients

Optimisation des DHT à partir des propriétés physiques, logiques et sociologiques des clients. Pierre Fraigniaud CNRS LRI, Univ. Paris-Sud http://www.lri.fr/~pierre. Plan. Distributed Hash Table (DHT) Structural properties Sociological properties Conclusion. Principles of DHTs. DHT.

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Optimisation des DHT à partir des propriétés physiques, logiques et sociologiques des clients

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  1. Optimisation des DHT à partir des propriétés physiques, logiques et sociologiques des clients Pierre Fraigniaud CNRS LRI, Univ. Paris-Sud http://www.lri.fr/~pierre

  2. Plan • Distributed Hash Table (DHT) • Structural properties • Sociological properties • Conclusion

  3. Principles of DHTs

  4. DHT • File, data, etc  name • Typically: name space = [0,1[ • h(file_name) = 0.10110001101 • User  name • User name  [0,1[ • h(my_IP@) = 0.0011010110

  5. Users = { } user x Data stored by x Correspondence 1 0

  6. y x x knows the IP@ of y and z z Overlay network 1 0

  7. Lookup 1 0 x h(Andrei Rublev)

  8. Entry point Node insertion 1 0

  9. Examples • CAN (D-dimensional meshes) • Chord (hypercube) • Viceroy (butterfly) • D2B, Koorde (de Bruijn) • …

  10. Structural Properties

  11. Desirable properties • Small number of hops for lookup: i.e., small diameter and efficient routing • Quick updates: i.e., small degree • Small congestion: i.e., small probability of contention

  12. length(overlay route) stretch = maxall routes length(Internet route) From the network point of view Taking the inter-node distance in Internet into account! It does not mean that closely related nodes must be close in the Overlay.

  13. Solution Theorem(Abraham & Malkhi) Under some conditions on the physical network,… …there exists an overlay network with strech 1+ε, degree and diameter O(log n).

  14. From the user point of view Taking the user interests into account! Closely related users aim at being close in the Overlay. How to measure proximity between users?

  15. Requests types • Typo: h(André Roublef) vs. h(Andrei Rublev) • Structure: Prefix search, interval, etc • Data-base type requests

  16. Sociological Properties

  17. Connect users sharing common interets • Gnutella enhanced with additional links… • Every user keeps links only with users sharing common interest (cf. Maay)

  18. Structure of user connections • Scale-free structure: Degree distribution = power law Prob( deg(x)=k ) ≈ k-a • Guided walk in scale-free graphs • Random walk • Shortest path • Neighbor with largest degree first

  19. Rumors and legends Path length Random walk Neighbor with highest degree first Shortest path Network size

  20. Using small world properties • Milgram’s experiment  six degrees of separation between indivitual • Kleinberg’s augmented meshes capture this phenomenon • DHT Symphony (!) • Why not just doing greedy routing?

  21. Conclusion

  22. Conclusion: users sociological properties seem to have more impact on DHT’s than network structural properties Unfortunately sociological properties are difficult to model and to measure Warning: this conclusion might be not true in other contexts, e.g., ad hoc, global computing, etc.

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