Davide Cherubini - PhD Thesis - UniCA Eprints

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AbstractThis dissertation investigates routing optimization in IP telecommunicationnetworks, under normal working conditions as well as underfailure conditions.The main objectives of the present optimization procedure are theminimization of the maximum link utilization in the network and toprovide a configuration that guarantees a 100% survivability degree.Traditionally two different steps are used to achieve this goal. Thefirst one aims to solve the well known “General Routing Problem(GRP)” [44] in order to find the optimal routing network configurationand, successively, a set of “optimal” backup paths is found inorder to guarantee network survivability. Furthermore, traditionalsurvivable techniques assume that the planning tasks are performedin a network control center while restoration schemes are implementeddistributively in network nodes.In this dissertation innovative linear programming models are presentedthat, making use of the Multi Protocol Label Switching (MPLS)techniques and IS-IS/OSPF IP routing protocol, melt routing and survivabilityrequirements. The models are extremely flexible, thus it ispossible to improve the objective function in order to fit itself to newerapplications and/or traffic typologies.The models presented in this dissertation help network service providersto optimize their network resources and to guarantee connectivity incase of failure, while still be able to offer a good quality of service.
Contents1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives of the dissertation . . . . . . . . . . . . . . . . . . . . 21.3 Dissertation structure . . . . . . . . . . . . . . . . . . . . . . . . . 32 Basic knowledge 42.1 Understanding IS-IS . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Understanding MPLS Technology . . . . . . . . . . . . . . . . . . 82.2.1 MPLS Traffic Engineering (MPLS TE) . . . . . . . . . . . 102.3 Failure analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.4 Restoration Schemes . . . . . . . . . . . . . . . . . . . . . . . . . 123 Literature Survey & Problem Statement 143.1 Optimization of IS-IS/OSPF routing . . . . . . . . . . . . . . . . 143.2 Optimization of MPLS routing . . . . . . . . . . . . . . . . . . . . 163.3 Survivability problem . . . . . . . . . . . . . . . . . . . . . . . . . 173.4 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Optimization Methods 194.1 Heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 194.1.1 Tabu search (TS) . . . . . . . . . . . . . . . . . . . . . . . 204.1.2 Genetic Algorithm (GA) . . . . . . . . . . . . . . . . . . . 214.1.3 Simulated Annealing (SA) . . . . . . . . . . . . . . . . . . 224.1.4 Ant Colony Optimization (ACO) . . . . . . . . . . . . . . 244.2 Deterministic Algorithms . . . . . . . . . . . . . . . . . . . . . . . 25v
Page 4 and 5: AcknowledgementsThis dissertation w
Page 9 and 10: CONTENTS7.4 The Real ISP’s Italia
Page 11 and 12: LIST OF FIGURES7.3 Default metrics
Page 13 and 14: 1.2 Objectives of the dissertation2
Page 15 and 16: Chapter 2Basic knowledgeThis chapte
Page 17 and 18: 2.1 Understanding IS-IS• A = sour
Page 19 and 20: 2.2 Understanding MPLS Technology2.
Page 21 and 22: 2.2 Understanding MPLS TechnologyTh
Page 23 and 24: 2.4 Restoration SchemesHowever, fai
Page 26 and 27: 3.1 Optimization of IS-IS/OSPF rout
Page 28: 3.3 Survivability problemhas been d
Page 31 and 32: 4.1 Heuristic Algorithmsmeta-heuris
Page 33 and 34: 4.1 Heuristic Algorithmsto keep a c
Page 35 and 36: 4.1 Heuristic Algorithmsprobability
Page 37 and 38: 4.3 Linear Programming ApproachExam
Page 39 and 40: 5.2 Traffic monitoring• 60% of th
Page 41 and 42: 5.2 Traffic monitoringExporting the
Page 43 and 44: 5.2 Traffic monitoringare included
Page 45 and 46: 5.2 Traffic monitoringthe Internet.
Page 47 and 48: 5.2 Traffic monitoring5.2.3 The net
Page 49 and 50: 5.2 Traffic monitoring5.2.4 Monitor
Page 51 and 52: 5.2 Traffic monitoringFigure 5.9: S
Page 53 and 54: Chapter 6LP Optimization ModelsThis
Page 55 and 56: 6.2 Graphs and Network Flowswith a
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6.2 Graphs and Network FlowsWhere B
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6.2 Graphs and Network Flows6.2.3 T
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6.3 Multicommodity Flow Problemscom
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6.4 LP modelsThe second layer intro
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6.4 LP modelsEquation 6.32 is the o
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6.4 LP modelsmalized as follows:z =
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6.4 LP models(a) Rerouting flow p
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6.4 LP models(a) 1 st solution(b) 2
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6.4 LP models∑f∈Fx f,lij⎛⎝i
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6.4 LP modelsterm is the MPLS part
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7.2 The highly meshed network7.2 Th
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7.3 The IBCN European network7.3 Th
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7.3 The IBCN European networkmaximu
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7.3 The IBCN European networkSurviv
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7.3 The IBCN European networkFigure
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7.4 The Real ISP’s Italian Backbo
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7.5 Conclusion and validation of re
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optimal configuration of network pa
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REFERENCES[14] Jones Lustig Format
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REFERENCES[40] A. V. Fiacco and G.
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REFERENCES[61] A. Nucci, B. Schroed
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Davide Cherubini - PhD Thesis - UniCA Eprints

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