1. 1 Asian Institute of Technology May 2009 MULTI-CONSTRAINED OPTIMAL PATH QUALITY OF SERVICE (QoS) ROUTING WITH INACCURATE LINK STATE INFORMATION AIT Master Thesis Competition 18 th May 2009 by Newton Perera Examination Committee: Dr. Teerapat Sanguankotchakorn (Chairman) Dr. R.M.A.P. Rajatheva Assoc. Prof. Tapio J. Erke

2. Contents 2 INTRODUCTION METHODOLOGY SIMULATION MODEL RESULTS CONCLUSION & CONTRIBUTION Asian Institute of Technology May 2009

3. 3 INTRODUCTION Asian Institute of Technology May 2009

4. Introduction Applications End Users Transport Network High Bandwidth Less delay, Latency Less Jitter Less Packet loss Less bandwidth No Guarantees No reservations Quality of Service WEB Streaming VoIP Social Media

5. 5 Asian Institute of Technology May 2009 BandwidthDelayJitter Packet Loss Transport Network Introduction ApplicationsEnd Users Multiple Constraints End-to-End Service Guarantee (QoS) Multi-Constrained QoS Routing Problem QoS etc.

6. 6 Asian Institute of Technology May 2009 BandwidthDelayJitter Packet Loss Transport Network Introduction ApplicationsEnd Users Multiple Constraints End-to-End Service Guarantee Multi-Constrained QoS Routing Problem Resource optimization Complexity of Algorithms Inaccuracy of Link states Objective: To find the Optimum path which satisfies Multiple Constraints

7. Multi-Constrained QoS Routing Three Main Concerns Multiple Constraints Path Optimization Inaccuracy of Link States Other Issues Accounting Intractability Reduce protocol overhead Reduce Complexity of routing algorithms Efficient handling of dynamic network environment Achieving high success rate in connectivity 7 Introduction Asian Institute of Technology May 2009

8. Less Complex but Slow Accurate but High message Overhead Hierarchical Routing Distributed Routing Source Routing Basic Solutions 8 Introduction Complex but Fast Centralized Less accurate Good for large Networks Aggregated network states High Imprecision Asian Institute of Technology May 2009

9. METHODOLOGY 9 Asian Institute of Technology May 2009

10. 10 Objectives: To Make it Fast Enough To Make it Accurate To Make it Better Utilized Methodology Source Routing Distributed Routing Combined Approach DHMCOP Asian Institute of Technology May 2009 Distributed Heuristic Multi-Constrained Optimal Path Algorithm (DHMCOP)

11. 11 Methodology Pruning Algorithm – For Bandwidth Constraint K – Shortest Path Algorithm – Path Selection Control Message Structure – Resource Reservation Hop Count – Path Optimization Combined Approach Asian Institute of Technology May 2009

12. Link Pruning & k- Dijkstra 12 Methodology Asian Institute of Technology May 2009

13. Pseudo code for the k-Dijkstra algorithm k = number of paths to find, n = paths found so far, s = source node, t = destination node, G[i,j] = network connectivity matrix, C[i,j] = network capacity matrix, H[u] = cost of a node, π[u] = predecessor vector, R[u] = Accumulated cost of node u Inf = a constant larger than the greatest possible path length Initialize G[i,j] and C[i,j] with network values Remove the span between s and t, to emulate a failure G[s,t] = inf, C[s,t] = 0 Call k-Dijkstra ( k, n, s, t, G, C ) k -Dijkstra ( k, n, s, t, G, C ) { while ( n<k ) { call Dijkstra algorithm with source, destination and the network graph Dijkstra ( s,t, G,C ) Record the path by tracing the predecessor vector Subtract every entry in G[i,j] that appears in the path n ++ } 13 then for m= 1,2,3….,M Relaxation process Methodology Asian Institute of Technology May 2009

14. Four main control messages 14 Methodology Check Resources Along the path Retain Accumulated Link metrics Probe Check & Reserve Resources Establish Connection Ack Release Resources along the path Flush all data Failure Follow the shortest path Terminate connection Nack Format of the probe messages Asian Institute of Technology May 2009

15. 15 Asian Institute of Technology May 2009 Methodology Connection Requests w1w1 w2w2 c1c1 c2c2 w1w1 w2w2 c1c1 c2c2 DHMCOP Source router Run k-Dijkstra Generate Probe Messages Destination Router Find Optimum path Send Ack Message Admission Control Resource Reservation w1w1 w2w2 c1c1 c2c2

16. SIMULATION MODEL 16 Asian Institute of Technology May 2009 Network Simulator V 2.29

17. Parameter values Network graphs are generated for 10-200 number of nodes Each link (u,v) is associated with randomly generated mean value for each weight Actual link weights are sampled from a normal distribution Constraint values are determined based on a minimum hop count value 1500, 2000 and 2500 requests were generated for graph with 10-50, 60-100 and 110-200 nodes Results are Averaged over 10 Random Graphs 17 Simulation Model Asian Institute of Technology May 2009 Nodes, Links (N,E) Feasible Paths (k) Complexity (λ) Constraints (m)

18. RESULTS 18 Asian Institute of Technology May 2009 Success Ratio Message Overhead Simulation time

19. SR for 10-200 nodes with k (m=2,λ=1) 19 Results Asian Institute of Technology May 2009

20. SR and Simulation time with k (N=100,m=2,λ≥1) 20 Results Asian Institute of Technology May 2009

21. 21 SR and Simulation time with k (N=100,λ=1,m≥1) Results Asian Institute of Technology May 2009

22. 22 Results Comparison of message overhead Asian Institute of Technology May 2009

23. 23 CONCLUSION & CONTRIBUTION Asian Institute of Technology May 2009

24. 24 Conclusion & Contribution DHMCOP algorithm best suits for the networks with nodes ranging from 50 to 150, when k is greater than 2. computational complexity is low for low values of λ Good performance can be achieved for the case of multiple constraints when more feasible paths are considered in the path search process. Message overhead is considerably low with comparison to flooding algorithms Asian Institute of Technology May 2009 Less Complex Fast Enough Less Overhead

25. 25 Conclusion & Contribution Conference Papers “Multi-Constrained Optimal path QoS Routing with Inaccurate Link State Information”- IEEE Global Communications Conference, 2009 (Submitted) Asian Institute of Technology May 2009

26. THANK YOU

27. BACKUP SLIDES

28. 28 Simulation Model Network Simulator 2 Traffic Agent LS Routing Protocol Dijkstra Algorithm Packet Classifier Packet Headers Asian Institute of Technology May 2009

29. 50 nodes100 nodes 200 nodes H_MCOP TAMCRA 29 K- No. of Feasible paths SR- Success Ratio

30. Success Ratio for nodes 10-50 30 Asian Institute of Technology May 2009

31. Success Ratio for nodes 10-200 31 Asian Institute of Technology May 2009

32. Variation of Success Ratio with k 32 Asian Institute of Technology May 2009

33. 33 Verified Simulation Asian Institute of Technology May 2009

34. Details of the paper Title: Multi-constrained Optimal Path Selection by Turgay Korkmaz and Marwan Krunz Algorithm: H_MCOP Method: Source routing Assumptions: True state of the network is available to each router Modification: Relaxation process of Dijkstra algorithm 34 Asian Institute of Technology May 2009

35. Algorithm for H_MCOP 35 Asian Institute of Technology May 2009

36. Simulation parameters 36 Random graphs for 50, 100 and 200 nodes Each link is associated with two weight parameters Link weights are selected from uniform distributions of positive, negative or no correlation w1(u,v) ~ uniform [1,100] and w2(u,v) ~ uniform [1,200] The primary cost of a link is taken as c(u,v) ~ uniform [1,200] Constraint values are taken as, c 1 ~ uniform [0.8w 1 (p 2 ), 1.2w 2 (p 2 )] and c 2 ~ uniform [0.8w 1 (p 1 ), 1.2w 2 (p 1 )] Asian Institute of Technology May 2009

37. Results comparison 37 Asian Institute of Technology May 2009

38. 38 Asian Institute of Technology August 2008 Request / Probe message

39. 39 Acknowledgement message Asian Institute of Technology May 2009

40. 40 Failure message Nack message Asian Institute of Technology May 2009

41. 41 Preserve already found feasible paths for future connection requests Upgrading LS protocol for link or node failures Multipath routing for load balancing Conclusion & Contribution Recommendations for future work Asian Institute of Technology May 2009