Overlay Network Creation and Maintenance with Selfish Users

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

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

3. Challenges v4 v1 v7 v5 v2 v6 v8 • What is the performance gain that can be achieved by a selfish node? p1=[v2v3v4v5v6v7v8v9] p8=[v1v2v3v4v5v6v7v9] v3 • What is the impact of selfish neighbor selection to overlay network performance? • What are the implications of selfish neighbor selection to system design? p3=[v1v2v4v5v6v7v8v9] v9 Selfish node p9=[v1v2v3v4v5v6v7v8]

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

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

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

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

9. k-median k-median: Find a subset I of F and a function σ:CI to min ( Σi,j sjcij )such that |I| ≤ k F: set of facilities C: set of clients, cij: cost connecting client jfacility I sj: demand of node j

10. Uncapacitated Facility Location Uncapacitated Facility Location (UFL): Find a subset I of F and a function σ:CI to min ( Σi fi + Σi,j sjcij ) F: set of facilities fi: cost to open facility C: set of clients, cij: cost connecting client jfacility I sj: demand of node j

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

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

13. SNS : the Game Game <V,{si},{Ci}> V : set of n players (nodes) {si}: strategies available to vi (wirings), choose k out of n to connect {Ci}: set of costs for vi 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. SNS : Equilibria n=15 k=2 k=3 k=8 k=11 Uniform Preference Skewness of preference In-degrees are highly skewed even under uniform preference ! Quality-based “preferential attachment” k (Link density)

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 Skewness of preference Skewness of preference Link density Link density

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

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

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

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

21. SNS vs. Heuristics: Social Cost Macroscopic view: Focusing on the social welfare The network is better off with selfish nodes!

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

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

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

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. EGOIST : Performance Best Response

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

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

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

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

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

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

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

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

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. 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. Reducing the Average Download Time Objective: Minimize the averagedownload time Max-Sum: Neighbor selection strategy of node vi: max (sum (MaxFlow(vi, vj)), for all vj

39. Reducing the Download Time Objective: Minimize the totaldownload time Max-Min: Neighbor selection strategy of node vi: max (min (MaxFlow(vi, vj)), for all vj

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

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

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

44. Server Selection Hardware server

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

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

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

48. DSM: Properties Convergence: Migration only if the cost of facilitating the demand decreases at least be a%, converges in O(log1+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. DSM: Evaluation • Similar results for UFL under different cost functions to open and maintain the server

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