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Overlay Network Creation and Maintenance with Selfish Users Georgios Smaragdakis Dissertation committee members: Azer Bestavros, Nikolaos Laoutaris, John Byers Overlays & Neighbor Selection Overlay node Overlay links Internet Transit ISP Transit ISP Access ISP Access ISP Access ISP Overlay applications: overlay routing, p2p file sharing, content distribution.. Focus on service quality! 2 Challenges v2 v1 v5 v4 v6 v7 v8 that What gain p1=[v2v3v4is v5v6the v7v8v9performance ] can be achieved by a selfish node? p8=[v1v2v3v4v5v6v7v9] Whatvis 3 the impact of selfish neighbor selection to overlay network p3=[v1v2v4v5v6v7v8v9] performance? v9 p9=[v node Selfish What are the implications of selfish 1v2v3v4v5v6v7v8] neighbor selection to system design? 3 Outline Implications Selfish Neighbor Selection to Overlay Routing Implications to File Sharing Implications to Service Provisioning 4 Implications Selfish Neighbor Selection to Overlay Routing Implications to File Sharing Implications to Service Provisioning 5 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 …] 6 Optimal Neighbor Selection vi: choose k neighbors, s.t. min C i ( S ) pij d S (vi , v j ) w v j Vi over all siSi vi u G-i=( V-i , S-i ) Set of residual nodes Set of residual wiring vi’s residual network 7 SNS & Facility Location Uniform link weights, and uniform preference k-median on asymmetric distances 8 k-median k-median: Find a subset I of F and a function σ:CI to min ( Σi,j sjcij ) such that |I| ≤ k F: set of facilities C: set of clients, cij: cost connecting client jfacility I sj: demand of node j 9 Uncapacitated Facility Location Uncapacitated Facility Location (UFL): Find a subset I of F and a function σ:CI to min ( Σi fi + Σi,j sjcij ) F: set of facilities fi: cost to open facility C: set of clients, cij: cost connecting client jfacility I sj: demand of node j 10 SNS & Facility Location Uniform link weights, and uniform preference k-median on asymmetric distances Since the wiring cost is the same w Non-uniform link weights, and uniform vi preference ILP formulation min Ci ( S ) p v j Vi ij u can be d S (vw,u i,vj ) obtained from k-median on reversed distances 11 Local Search (LS) vi: choose k neighbors min Ci ( S ) p v j Vi ij d S (vi , v j ) w over all siSi vi [Arya et al,STOC’01] u G-i=( V-i , S-i ) Set of residual nodes Set of residual wiring vi’s residual network 12 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 C i ( S ) pij d S (vi , v j ) v j Vi 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 13 SNS : Equilibria Uniform Preference n=15 Skewness of preference k=2 k=3 k=8 k=11 k (Link density) In-degrees are highly skewed even under uniform preference ! Quality-based “preferential attachment” 14 SNS : Efficiency Performance of ILP & LS is close to Utopian! Link density Skewness of preference Link density Skewness of preference Theoretical results showed in the worst case the cosial cost can be bad [Laoutaris, Poplawsi, Rajaraman, Sundaram, Teng,PODC’08] 15 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) 16 Connecting on a k-Random graph BRITE (n=50) 0 2 3 5 k 11 22 PlanetLab (n=50) 0 2 3 5 11 k 22 AS-Level (n=50) 0 2 3 5 11 22 k If your neighbors are naïve, it pays to be selfish! 17 Connecting on a k-Closest graph BRITE (n=50) 0 2 3 5 11 22 PlanetLab (n=50) 0 2 3 k 5 11 k 22 AS-Level (n=50) 0 2 3 5 11 22 k “Greed is not good” If your neighbors are greedy, it pays to be selfish! 18 Connecting on a k-Regular graph BRITE (n=50) 0 2 3 5 k 11 22 PlanetLab (n=50) 0 2 3 5 11 22 k AS-Level (n=50) 0 2 3 5 11 22 k “Common pattern is not good” If your neighbors have the same wiring pattern, it pays to be selfish! 19 Connecting on a Best Response graph BRITE (n=50) 0 2 3 5 k 11 22 PlanetLab (n=50) 0 2 3 5 11 22 k AS-Level (n=50) 0 2 3 5 11 22 k The BR graph is highly optimized! If your neighbors are selfish, it is OK to be naïve! 20 SNS vs. Heuristics: Social Cost Macroscopic view: Focusing on the social welfare (k=2) k-Random/BR k-Closest/BR k-Regular/BR BRITE 1.44 1.53 3.61 PlanetLab 2.23 1.48 3.84 AS 2.04 1.90 4.78 The network is better off with selfish nodes! 21 Real-Time Applications Min-Max Best Response Worst delay in the overlay: 0 2 3 5 11 22 k 22 SNS with Variable Degree Real-time applications 100 links Variable degree through LS: Swap 1 link Add 1 link Drop 1 link 120 links Application requirement (Performance when k=5, n=50 i.e. 250 links) 23 Implications Selfish Neighbor Selection to Overlay Routing Implications to File Sharing Implications to Service Provisioning 24 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 25 EGOIST : Performance Best Response 26 EGOIST: Passive Measurements Passive measurements based on virtual coordinates (pyxida system) with minimal cost 27 EGOIST: Other Metrics End-to-end available bandwidth (pathchirp) with minimal measurement overhead CPU load (loadavg) 28 EGOIST: Marginal Utility of Rewiring BR Lazy BR (threshold = 10%) There exists a performance knee (k=3 or 4) Re-wirings could be reduced with lazy BR 29 quality Connectivity Efficiency Index EGOIST: Effect of Churn Connectivity is guaranteed (in T/n time) HybridBR (a connected ring is maintained) delivers much of the efficiency of BR 30 quality Connectivity Efficiency Index EGOIST: Effect of Churn BR and Hybrid BR dominate all the other heuristics HybridBR pays off at high churn 31 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. 32 Implications Selfish Neighbor Selection to Overlay Routing Implications to File Sharing Implications to Service Provisioning 33 Modern File Sharing Systems Parallel upload/ download Internet Seeder Transit ISP Transit ISP Access ISP Access ISP Access ISP - Swarming Local scheduling - Local Rarest First Flat connectivity - Choke/unchoke Leecher Overlay node 34 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 35 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 36 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 37 Reducing the Average Download Time Objective: Minimize the average download time Max-Sum: Neighbor selection strategy of node vi: max (sum (MaxFlow(vi, vj)), for all vj 38 Reducing the Download Time Objective: Minimize the total download time Max-Min: Neighbor selection strategy of node vi: max (min (MaxFlow(vi, vj)), for all vj 39 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 40 Performance Evaluation Max-Sum Max-Min Delivery Time Node ID Naive Selfish Upload: Protects the uplink File ID capacity of the slow node File ID Improves the download time in the system File ID Flattens distribution time! Guarantees synchronization! Comparable average download time 41 Other Work: File Searching Best response: max #nodes reached 4 Bootstrap 1 Server 3 6 5 2 selfishly TTL of scoped flooding is 2 Maximum Coverage Problem 42 Implications Selfish Neighbor Selection to Overlay Routing Implications to File Sharing Implications to Service Provisioning 43 Server Selection Hardware server 44 Centralized Deployment Generic Service Host Software server Demand change e.g. Flash crowd, time-of-day 45 effect Dynamic Service Deployment Generic Service Host Software server Demand change e.g. Flash crowd, time-of-day 46 effect Distributed Service Migration (DSM) “ring” nodes r-ball (r=2) Solve k-median or UFL in an r-ball ..BUT nodes outside the r-ball are totally neglected Iterate until convergence 47 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 48 DSM: Evaluation Similar results for UFL under different cost functions to open and maintain the server 49 Dynamic vs. Static Deployment Static deployment DSM DSM Dynamic deployment 50 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! 51 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 52 Thank You! http://csr.bu.edu/sns http://csr.bu.edu/dfl 53