1. 1 SLIC: A Selfish Link-based Incentive Mechanism for Unstructured P2P Networks Qixiang Sun Hector Garcia-Molina Stanford University

2. 2 P2P File Sharing Gnutella, KaZaA, Overnet, etc. Internet

3. 3 Architecture

4. 4 Problem Why share files? Why forward other’s queries? Nodes are SELFISH!

5. 5 Existing Approaches Micro-payments/Bartering “Global” reputation/trust system Query Results 0.8 0.2 A B C AB Reputation = f (G)

6. 6 Our Approach AB Fragment A Fragment B They need each other to reach more nodes.  Can retaliate

7. 7 Our Approach (2) A DCB Reward “good” neighbors Penalize “bad” neighbors

8. 8 Simple Model of a Node Capacity - Inject new queries - Answer/Forward queries Answering power Assume operate in rounds

9. 9 SLIC Algorithm A DCB W(A,B) W(A,C) W(A,D) (2) Use the weights to “divide” its “spare” capacity (1) Adjust weights based on quality of services During each round:

10. 10 SLIC Algorithm (2) A DCB 4 hits 2 hits For each query: # hits  [0, 1] Service = sum scores over all queries (0.25) (0.5) Service per round: Computed over A’s queries whose TTLs have just expired this round

11. 11 SLIC Algorithm (3) W (A,B) = 0.9 W (A,B) + 0.1 S (A,B) ii-1i Update weight with exponential decay Allocate spare capacity proportionally W(A,B) W(A,B) + W(A,C) + W(A,D) A B C E.g., node B gets D

12. 12 Does it work? Can a malicious node take advantage of the system? –Share less files –Dedicate less capacity –Have fewer connections

13. 13 Utility Average # of hits per query Total # of hits

14. 14 Evaluation Setup baseline = all nodes behave “normally” choose a probe node to behave differently –vary the probe node location compare the difference in utility –improvement ratio Simulation using 250-nodes random graphs

15. 15 Answering Power Baseline

16. 16 Total Capacity Baseline

17. 17 Connectivity

18. 18 Dynamic Scenario A B C D ? W = ? Can malicious nodes take advantage? “Happy” nodes do not want new connections while “unhappy” nodes take more risks

19. 19 Dynamic Scenario (2) Weight for a new link = average weight of the existing links –Inverse of the current utility: 1/U –Exponential of the current utility: e -U Drop links with small weights

20. 20 Evaluation Setup A probe node joins late How does its utility change over time?

21. 21 Join (1)

22. 22 Join (2)

23. 23 Respawn Every node periodically tries to establish new links to improve utility 3 group of nodes –Normal –Low Answer Power –High Rho

24. 24 Respawn (2) Better service

25. 25 Future Direction All nodes selfishly change parameters to maximize their utility at the cheapest cost Simplify model for game-theoretic analysis Extend SLIC to other search mechanisms (e.g., random walks) and beyond searches

26. 26 Conclusion SLIC is a “retaliation-based” mechanism Locally selfish decisions can give rise to a proper incentive structure Accepting new connections based on own utility can reduce the impact of malicious node

27. 27 More Information http://www-db.stanford.edu/~qsun Google for “Stanford Peers”

28. 28

29. 29 # of New Queries

30. 30 # of New Queries (2)