1. Overlay Network Creation and Maintenance with Selfish Users Georgios Smaragdakis Dissertation committee members: Azer Bestavros, Nikolaos Laoutaris, John Byers

2. 2 Overlay applications: overlay routing, p2p file sharing, content distribution.. Access ISP Transit ISP Overlays & Neighbor Selection Internet Overlay links Transit ISP Access ISP Overlay node Focus on service quality!

3. 3 Challenges v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 p 1 =[v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 9 ] p 9 =[v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 ] p 3 = [v 1 v 2 v 4 v 5 v 6 v 7 v 8 v 9 ] p 8 =[v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 9 ] Selfish node  What is the performance gain that can be achieved by a selfish node?  What is the impact of selfish neighbor selection to overlay network performance?  What are the implications of selfish neighbor selection to system design?

4. 4 Selfish Neighbor Selection Implications to Overlay Routing Implications to File Sharing Implications to Service Provisioning Outline

5. 5 Selfish Neighbor Selection Implications to Overlay Routing Implications to File Sharing Implications to Service Provisioning

6. 6 Selfish Neighbor Selection (SNS)  Constraints that need to be addressed in a realistic model for overlay networks:  Bounded degree  Preference vectors  Realistic network distance  Link directionality  Fundamentally different from other models that have been proposed for other networks. [Fabrikant et al.,PODC’03; Chun et al., Infocom’04 …]

7. 7 Optimal Neighbor Selection v i : choose k neighbors, s.t. vivi G -i =( V -i, S -i ) u w min over all s i  S i v i ’s residual network Set of residual nodes Set of residual wiring

8. 8 SNS & Facility Location  Uniform link weights, and uniform preference  k-median on asymmetric distances

9. 9 k-median  k-median: Find a subset I of F and a function σ:C  I to min ( Σ i,j s j c ij ) such that |I| ≤ k F: set of facilities C: set of clients, c ij : cost connecting client j  facility I s j : demand of node j

10. 10 Uncapacitated Facility Location  Uncapacitated Facility Location (UFL): Find a subset I of F and a function σ:C  I to min ( Σ i f i + Σ i,j s j c ij ) F: set of facilities f i : cost to open facility C: set of clients, c ij : cost connecting client j  facility I s j : demand of node j

11. 11  Non-uniform link weights, and uniform preference  ILP formulation SNS & Facility Location  Uniform link weights, and uniform preference  k-median on asymmetric distances u w w,u can be obtained from k-median on reversed distances w u vivi min Since the wiring cost is the same

12. 12 Local Search (LS) v i : choose k neighbors vivi u w min over all s i  S i v i ’s residual network [Arya et al,STOC’01] G -i =( V -i, S -i ) Set of residual nodes Set of residual wiring

13. 13 SNS : the Game  Game  V : set of n players (nodes)  {s i }: strategies available to v i (wirings), choose k out of n to connect  {C i }: set of costs for v i min  Best response of a node: node’s optimal wiring  Outcome: S, the global wiring  A stable wiring is a pure Nash equilibium  Using iterative best response  Fundamentally different from selfish routing

14. 14 SNS : Equilibria n=15 k=2 k=3 k=8 k=11 Uniform Preference Skewness of preference k (Link density) In-degrees are highly skewed even under uniform preference !  Quality-based “preferential attachment”

15. 15  Performance of ILP & LS is close to Utopian!  Theoretical results showed in the worst case the cosial cost can be bad [Laoutaris, Poplawsi, Rajaraman, Sundaram, Teng,PODC’08] SNS : Efficiency Link density Skewness of preference Link density Skewness of preference

16. 16 SNS : Trace-Driven Evaluation  How we assign the distance:  Synthetically using BRITE  Empirically from PlanetLab  Empirically from AS-level maps [Routeviews]  Neighbor Selection Strategies:  k-Random heuristic  k-Closest heuristic  k-Regular heuristic  k-Best Response  Control parameter:  Bound on out-degree k (link density)

17. 17 Connecting on a k-Random graph k k k AS-Level ( n=50 )PlanetLab ( n=50 ) BRITE ( n=50 ) If your neighbors are naïve, it pays to be selfish! 0 2 3 5 11 22

18. 18 Connecting on a k-Closest graph k k k 0 2 3 5 11 22 If your neighbors are greedy, it pays to be selfish!  “Greed is not good” AS-Level ( n=50 )PlanetLab ( n=50 ) BRITE ( n=50 )

19. 19 Connecting on a k-Regular graph k k k 0 2 3 5 11 22 If your neighbors have the same wiring pattern, it pays to be selfish!  “Common pattern is not good” AS-Level ( n=50 )PlanetLab ( n=50 ) BRITE ( n=50 )

20. 20 Connecting on a Best Response graph  The BR graph is highly optimized! k k k 0 2 3 5 11 22 AS-Level ( n=50 )PlanetLab ( n=50 ) BRITE ( n=50 ) If your neighbors are selfish, it is OK to be naïve !

21. 21 SNS vs. Heuristics: Social Cost  Macroscopic view: Focusing on the social welfare The network is better off with selfish nodes! (k=2)k-Random/BRk-Closest/BRk-Regular/BR BRITE1.441.533.61 PlanetLab2.231.483.84 AS2.041.904.78

22. 22 Real-Time Applications  Min-Max Best Response Worst delay in the overlay: k 0 2 3 5 11 22

23. 23 SNS with Variable Degree  Real-time applications  Variable degree through LS:  Swap 1 link  Add 1 link  Drop 1 link Application requirement (Performance when k=5, n=50 i.e. 250 links) 100 links 120 links

24. 24 Selfish Neighbor Selection Implications to Overlay Routing Implications to File Sharing Implications to Service Provisioning

25. 25 Basic design of EGOIST: Link state protocol Measurements of distance to candidate neighbors Wirings according to chosen strategy Re-wirings every T second A newcomer bootstraps by connecting to arbitrary neighbors

26. 26 EGOIST : Performance Best Response

27. 27 EGOIST: Passive Measurements  Passive measurements based on virtual coordinates (pyxida system) with minimal cost

28. 28 EGOIST: Other Metrics  End-to-end available bandwidth (pathchirp) with minimal measurement overhead  CPU load (loadavg)

29. 29 EGOIST: Marginal Utility of Rewiring  There exists a performance knee (k=3 or 4)  Re-wirings could be reduced with lazy BR BR Lazy BR (threshold = 10%)

30. 30 EGOIST: Effect of Churn  Connectivity is guaranteed (in T/n time)  HybridBR (a connected ring is maintained) delivers much of the efficiency of BR Efficiency Index Connectivity quality

31. 31 EGOIST: Effect of Churn  BR and Hybrid BR dominate all the other heuristics  HybridBR pays off at high churn Efficiency Index Connectivity quality

32. 32 EGOIST : Other Work  CPU and memory load is very low  Robust to cheating  Scalability  via topological sampling  via layered architecture  Applications including multi-player P2P games, real-time traffic over IP etc.

33. 33 Selfish Neighbor Selection Implications to Overlay Routing Implications to File Sharing Implications to Service Provisioning

34. 34 Access ISP Transit ISP Modern File Sharing Systems  Parallel upload/ download - Swarming  Local scheduling - Local Rarest First  Flat connectivity - Choke/unchoke Internet Transit ISP Access ISP Overlay node Seeder Leecher

35. 35 n-way Broadcast Internet  Synchronization - Distributed databases - Backups  Batch parallel processing - The files have to be received by all nodes before the next step of processing begins

36. 36 Preliminary Solutions  n co-existing swarms (-) Stress of physical links (-) Exchange of multiple chunks in parallel overpartitions the uplink capacity [Tian et al., ICPP’06]  End-system multicast (mesh) [SplitStream, Bullet] (-) Creates an overlay for each swarm (-) No coordination among swarms (-) Monitor overhead

37. 37 Design Strategies for n-way Broadcast  Joint optimization of upload/download while participating in many swarms  Data Agnostic - Keeps swarming and local scheduling  Bandwidth-Centric - Max-flow to approximate swarming behavior [Massoulie et al., Infocom’07]  Bounded Degree

38. 38 Reducing the Average Download Time  Objective: Minimize the average download time Max-Sum:  Neighbor selection strategy of node v i : max (sum (MaxFlow(v i, v j )), for all v j

39. 39 Reducing the Download Time  Objective: Minimize the total download time Max-Min:  Neighbor selection strategy of node v i : max (min (MaxFlow(v i, v j )), for all v j

40. 40 Optimized Graphs and Swarming  Formation of stable graphs  Each node strives to improve both the upload and download flow  Performance of swarming on optimized graphs - Max flow might not be realizable

41. 41 Performance Evaluation File ID Node ID Delivery Time Naive Max-Sum Max-Min File ID  Flattens distribution time!  Guarantees synchronization!  Comparable average download time Selfish Upload: Protects the uplink capacity of the slow node  Improves the download time in the system

42. 42 Other Work: File Searching  Best response: max #nodes reached Bootstrap Server 1 2 3 4 5 6 TTL of scoped flooding is 2  Maximum Coverage Problem selfishly

43. 43 Selfish Neighbor Selection Implications to Overlay Routing Implications to File Sharing Implications to Service Provisioning

44. 44 Server Selection Hardware server

45. 45 Centralized Deployment Generic Service Host Software server Demand change e.g. Flash crowd, time-of-day effect

46. 46 Dynamic Service Deployment Generic Service Host Software server Demand change e.g. Flash crowd, time-of-day effect

47. 47 r-ball (r=2) Distributed Service Migration (DSM)  Solve k-median or UFL in an r-ball ..BUT nodes outside the r-ball are totally neglected “ring” nodes  Iterate until convergence

48. 48 DSM: Properties  Convergence: Migration only if the cost of facilitating the demand decreases at least be a%, converges in O(log 1+a n) steps  We can control the speed of convergence by tuning a  Limited horizon view requirement:  r regulates the trade-off between scalability and performance

49. 49  Similar results for UFL under different cost functions to open and maintain the server DSM: Evaluation

50. 50 Dynamic vs. Static Deployment Static deployment Dynamic deployment DSM

51. 51 Conclusions  What is the performance gain that can be achieved by a selfish node?  Selfish nodes can reap substantial performance gain.  What is the impact of selfish neighbor selection to overlay network performance?  Surprisingly, the evolving graphs have also good performance!

52. 52 Conclusions  What are the implications of selfish neighbor selection to system design?  Selfish wiring strategies are easily realizable  Selfish wiring behavior can be used towards distributed overlay network creation and maintenance  Selfish wiring must be a component of any system to protect it from abuse  Selfish wiring behavior can be used for efficient dynamic service provisioning

53. 53 Thank You! http://csr.bu.edu/sns http://csr.bu.edu/dfl