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Virtual Network Embedding with Coordinated Node and Link Mapping

Virtual Network Embedding with Coordinated Node and Link Mapping. N. M. Mosharaf Kabir Chowdhury Muntasir Raihan Rahman and Raouf Boutaba University of Waterloo. Motivation. Network Virtualization Coexistence of multiple virtual networks (VNs) over a shared substrate Applications

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Virtual Network Embedding with Coordinated Node and Link Mapping

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  1. Virtual Network Embedding with Coordinated Node and Link Mapping N. M. Mosharaf Kabir Chowdhury Muntasir Raihan Rahman and Raouf Boutaba University of Waterloo

  2. Motivation • Network Virtualization • Coexistence of multiple virtual networks (VNs) over a shared substrate • Applications • Research experiments • Future Internet architecture (e.g., Clean-slate) • Major challenge • Efficient assignment of substrate network resources to virtual network’s requirements INFOCOM 2009 @ Rio de Janeiro, Brazil

  3. VN Embedding Problem • Given: • Single substrate network: GS = (NS, ES) • Online VN requests: GV = (NV, EV) • Requirements and Constraints of virtual nodes and virtual links • Task: • Assign virtual nodes and links to substrate nodes and links • Allocate resources • CPU, bandwidth INFOCOM 2009 @ Rio de Janeiro, Brazil

  4. VN Embedding Objectives • Maximize • Acceptance ratio • Percentage of request accepted • Revenue • Based on resources requested for a VN • Minimize • Cost • Based on substrate network resources allocated for embedding VN requests INFOCOM 2009 @ Rio de Janeiro, Brazil

  5. Virtual Network Embedding 10 80 55 15 a a A B 10 12 22 b b c c 12 10 NP-hard 90 50 10 10 10 20 C D E F 15 60 85 20 27 25 e e 5 5 G H 17 d d f f 70 65 20 20 INFOCOM 2009 @ Rio de Janeiro, Brazil

  6. Observations • Existing heuristics: • Disjoint node and link mapping • Ignore (completely) location constraints on virtual nodes • Our approach • Node mapping influences link mapping • Location constraints take the front seat • Meta-VN request INFOCOM 2009 @ Rio de Janeiro, Brazil

  7. Substrate Graph Augmentation ∞ a c ∞ 80 55 15 A B ∞ ∞ 22 ∞ 10 12 a 90 50 10 20 10 12 C D E F 15 b c 60 85 10 27 25 10 10 G H 17 70 65 ∞ ∞ b INFOCOM 2009 @ Rio de Janeiro, Brazil

  8. Mixed Integer Program • Objective • Flow variables: • Multi-commodity flow constraints • Binary variables: • Exactly one substrate node is selected for each meta-node • At most one meta-node is mapped onto a substrate node α, β: Tuning parameters R: Remaining capacity INFOCOM 2009 @ Rio de Janeiro, Brazil

  9. Mixed Integer Program Solution ∞ a c ∞ 80 55 15 A B ∞ ∞ 22 ∞ 10 12 NP-hard a 90 50 10 20 10 12 C D E F 15 b c 60 85 10 27 25 10 10 G H 17 70 65 ∞ ∞ b INFOCOM 2009 @ Rio de Janeiro, Brazil

  10. LP Relaxation • Relax the binary constraints on the x variable • Problem 1:x values do not select single substrate nodes for every meta-node • Use rounding techniques (e.g., deterministic, randomized) • Problem 2:x values are inconsistent • Use the product of x and f while rounding INFOCOM 2009 @ Rio de Janeiro, Brazil

  11. ViNEYard (D-ViNE & R-ViNE) For each VN request: • Augment the substrate graph • Solve the resulting LP • For each virtual node: • Calculate the probability for each meta-node to be selected for the corresponding virtual node • Selection: • D-ViNE: Select the meta-link with the highest probability • R-ViNE: Select a meta-link randomly with the calculated probability • Use MCF to map virtual edges • If the VN request is accepted • Update residual capacities of the substrate resources INITIALIZATION NODE MAPPING LINK MAPPING FINALIZATION INFOCOM 2009 @ Rio de Janeiro, Brazil

  12. Performance Evaluation INFOCOM 2009 @ Rio de Janeiro, Brazil

  13. Simulation Setup • Substrate network • 50 nodes in a 25x25 grid with 0.5 link probability • CPU/BW uniformly distributed in the range: 50-100 units • VN requests • Poisson arrival rates from 4 VN requests 100 time units • Exponentially distributed lifetime of 1000 time units • 2-10 nodes with 0.5 link probability • Tools: GT-ITM (Georgia Tech Internet Topology Models), GLPK (GNU Linear Programming Toolkit) INFOCOM 2009 @ Rio de Janeiro, Brazil

  14. Acceptance Ratio INFOCOM 2009 @ Rio de Janeiro, Brazil

  15. Revenue Vs Cost Revenue Cost INFOCOM 2009 @ Rio de Janeiro, Brazil

  16. Link Utilization INFOCOM 2009 @ Rio de Janeiro, Brazil

  17. Conclusions • ViNEYard Algorithms • Improved correlation between the node mapping and the link mapping phases • Increased acceptance ratio and revenue with decreased cost • Ongoing and Future Work • Window-based extension to D-ViNE and R-ViNE a.k.a. WiNE • Extension to inter-domain scenario a.k.a. PolyViNE • Approximation factors for D-ViNE and R-ViNE INFOCOM 2009 @ Rio de Janeiro, Brazil

  18. Thank You! Questions? http://www.mosharaf.com/ INFOCOM 2009 @ Rio de Janeiro, Brazil

  19. Backup Slides INFOCOM 2009 @ Rio de Janeiro, Brazil

  20. INFOCOM 2009 @ Rio de Janeiro, Brazil

  21. INFOCOM 2009 @ Rio de Janeiro, Brazil

  22. D-ViNE INFOCOM 2009 @ Rio de Janeiro, Brazil

  23. R-ViNE INFOCOM 2009 @ Rio de Janeiro, Brazil

  24. Resource Utilization Node Utilization Link Utilization INFOCOM 2009 @ Rio de Janeiro, Brazil

  25. Effect of Increasing Load INFOCOM 2009 @ Rio de Janeiro, Brazil

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