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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

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

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3 INTRODUCTION Asian Institute of Technology May 2009

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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

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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.

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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

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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

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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

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METHODOLOGY 9 Asian Institute of Technology May 2009

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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)

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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

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

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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, [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