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Enabling Inter-domain DTN Communications by Networked Static Gateways

Enabling Inter-domain DTN Communications by Networked Static Gateways Ting He*, Nikoletta Sofra † , Kang-Won Lee*, and Kin K Leung † * IBM † Imperial College Sept. 2009. Introduction. Different DTN domains call for different technology E.g., coalition operations, MESSAGE project. d. s.

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Enabling Inter-domain DTN Communications by Networked Static Gateways

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  1. Enabling Inter-domain DTN Communications by Networked Static Gateways Ting He*, Nikoletta Sofra†, Kang-Won Lee*, and Kin K Leung† * IBM † Imperial College Sept. 2009

  2. Introduction • Different DTN domains call for different technology • E.g., coalition operations, MESSAGE project d s (b) Heterogeneous sensor networks (a) Coalition networks : candidate gateway location 2

  3. Introduction d d s s (a) (b) : gateway Q: How to deploy them? 3 Gateway deployment influences performance

  4. Domain Heterogeneity • What factors to consider: • Inter-domain factors: • Traffic demands • Inter-domain routing scheme • Policy • Intra-domain factors: • Mobility, channel, radio tech/range → contact patterns • Node population/density • Routing scheme: • Replication strategy: forwarding, limited/unlimited replication • Queue discipline • Resource assumption: unlimited/limited bandwidth/buffer • Others: data ferries, network coding, etc. 4

  5. Outline 5 • Unified Gateway Deployment Framework (UGDF) • Utility computation • Gateway placement • Context-aware utility computation • Performance evaluation

  6. Unified Gateway Deployment Framework (UGDF) Domain knowledge, Performance criteria Utility computation Gateway placement • Utility computation: Decomposition + domain-specific calculation • Utility decomposition: • Uglobal = Σdomain i.jλij [Σρp (Σhop k Uk )] • λij: inter-domain traffic demand; ρp: load factor (for inter-domain routing) • Per-hop utility calculation: domain-specific • Note: Utilities in different domains should be independent (guaranteed by “networked gateways”) U(L’) L* = argmaxL’ U(L’) s.t. cost(L’) ≤ C Budget C 6

  7. Unified Gateway Deployment Framework (UGDF) • Gateway placement: • max U(L’) ≠ ΣL’ U(li)! (harder than knapsack problem) • s.t. Σli∊L’ cost(li) ≤ C • Optimal alg: unequal cost – NP-hard, equal cost – O(Lg) • Greedy alg: While cost less than C • l(j) = argmaxL\L’ [U(li U L’)-U(L’)]/ci • L’ ← L’ U l(j) • Backward greedy alg: While cost greater than C • l(j) = argminL’ [U(L’)-U(L’ \ {li})]/ci • L’ ← L’ \ {l(j)} Domain knowledge, Performance criteria Utility computation Gateway placement U(L’) L* = argmaxL’ U(L’) s.t. cost(L’) ≤ C Budget C 7

  8. Unified Gateway Deployment Framework (UGDF) • Gateway placement (cont’d): • max U(L’) • s.t. Σli∊L’ cost(li) ≤ C • Performance guarantee: Under equal cost: • Greedy/backward greedy soln’s are ε-close to the optimal if [U(l U L’)-U(L’)]’s are ε-close (for all l), i.e. • [U(l U L1’)-U(L1’)] ≥ (1- ε) [U(l U L2’)-U(L2’)] • for |L1’|=|L2’|. Domain knowledge, Performance criteria Utility computation Gateway placement U(L’) L* = argmaxL’ U(L’) s.t. cost(L’) ≤ C Budget C 8 8

  9. Unified Gateway Deployment Framework (UGDF) • Sketch of proof: (equal cost) • Decompose the total utility: (i: ‘g’ for greedy, ‘o’ for optimal) • U(Li) = U(li1) + U(li2|li1) +…+ U(lig|li1,…,lig-1) • By definition of the greedy alg: • U(lgj|lg1,…,lgj-1) ≥ U(loj|lg1,…,lgj-1) • By the condition: • U(loj|lg1,…,lgj-1) ≥ (1-ε) U(loj|lo1,…,loj-1) • Combining both gives • U(Lg) ≥ (1- ε)U(Lo). • Similarly, • U(Ltotal) - U(Lbg) ≤ [U(Ltotal) - U(Lo)] / (1- ε). □ • A similar result holds for unequal costs. 9 9

  10. Outline 10 • Unified Gateway Deployment Framework (UGDF) • Context-aware utility computation • Results & sketch of analysis • Performance evaluation

  11. Context-aware Utility Computation • Assume Poisson contact processes. (node-node: λn; node-gateway: λl) • Source-gateway hop: • Single-copy routing/forwarding: • Delay: 1/λl • # replicas: 1 • Unlimited replication: • Delay ≈ N\logN(1/ λl+1/ λn) • # replicas≈ (1+N)/2 • Limited replication: • Delay≈ F(N, λl, λn, r) • # replicas≈ N\(r+1)(N-r/2) • Other hops: • Intermediate domain: (same) • Destination domain: (similar but λl→λn) 11

  12. Context-aware Utility Computation • Sketch of analysis:For unlimited replication: • Decompose: • E[Delay] = ∑j P{delivery between jth and (j+1)th replications}.E[Delay|▲] • (▲) • E[# replicas] = ∑jP{▲} . (j+1) • Note: • Period between jth and (j+1)th replications ~ Exp((j+1)(N-j-1)λn) • Conditioned on ▲, additional delay after jth replication ~ Exp((j+1)λl) • 2. Bound: • P{▲} = F1(N,j,λn,λl) • F2(N,j,λn,λl) ≤ E[Delay|▲] ≤F3(N,j,λn,λl) • 3. Approximate at large N (actually close even at N=5) • Similar steps for limited replication. □ 12

  13. Outline 13 • Unified Gateway Deployment Framework (UGDF) • Context-aware utility computation • Performance evaluation • Synthetic simulations • Trace-driven simulations

  14. Performance Evaluation • Synthetic simulations: • Setup: • Two coalition networks with different bases (localized random walks) • Size, mobility, routing vary independently • Calculated vs. simulated utilities: • Contact processes not Poisson • Still good approximation (scaling needed for direct delivery) 14

  15. Performance Evaluation • Synthetic simulations (cont’d): • End-to-end performance: • 6 strategies (3 optimization alg’s, 2 utility computation methods) • Greedy/backward greedy alg + calculated utility is near optimal • Results robust against routing schemes and utility measure Minimize delay Minimize # replicas (unlimited replication in domain 1, direct delivery in domain 2) 15

  16. Performance Evaluation • Trace-driven simulations: • Setup: • Extracting traces from Dieselnet trace*: 4 sets of two-domain traces of mobile-to-mobile and mobile-to-AP contacts; 10 candidate gateway locations; 3 nodes per domain • Uniform traffic: 5 packets per hour per source node Mobile-mobile Mobile-AP 16 * http://traces.cs.umass.edu/, Dieselnet Fall 2007

  17. Performance Evaluation • Trace-driven simulations (cont’d): • Accuracy of utility calculation: Good approximation of the trend (under constant scaling). Avg. # replicas (unlimited replication) Avg. delay (direct delivery, unlimited replication) 17

  18. Performance Evaluation • Trace-driven simulations (cont’d): • Performance of deployment: • Near optimal (again) • Much better (30%) than utility-agnostic deployment Minimize # replicas Minimize delay (both under unlimited replication) 18

  19. Summary 19 • Gateway deployment for inter-domain DTN • UGDF: utility computation, gateway placement • Context-aware utility computation: decomposition & domain-specific analysis • Observations: • Poisson contacts? → Robust to mobility models (up to scaling) • Suboptimal alg’s? → Near-optimal performance (for scattered candidate locations) • Gap with oracle? → Good deployment relies on predictable mobility and representative training data

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