Davide Cherubini - PhD Thesis - UniCA Eprints

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REFERENCES[28] L. S. Buriol, M. G. C. Resende, and M. Thorup. Survivable IPnetwork design with OSPF routing. Networks, 49(1):51–64, 2007. 17[29] V. Cerny. A thermodynamical approach to the traveling salesman problem:an efficient simulation algorithm. Optimization Theory and Appl., 45:41–51,1985. 22[30] T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction toAlgorithms. MIT Press, 1990. 47[31] G. B. Dantzig. Linear Programming and Extensions. Princeton UniversityPress, Princeton, 1963. 43[32] Edsger W. Dijkstra. A note on two problem in connexion with graphs.Numerische Mathematik, 1:269–271, 1959. 5, 49[33] M. Dorigo and G. Di Caro. New Ideas in Optimization: The Ant ColonyOptimization Meta-Heuristic. pages 11–32. McGraw-Hill, 1999. 24[34] Marco Dorigo. Optimization, learning and natural algorithms. <strong>PhD</strong> thesis,Dipartimento di Elettronica, Politecnico di Milano, Milan, 1992. 24[35] Marco Dorigo and Thomas Stützle. Ant Colony Optimization. TheMIT Press, 2004. 24[36] U. Killat E. Mulyana. A Hybrid Genetic Algorithm Approach for OSPFWeight Setting Problem. In Peroc. of the 2 nd Polish-German TeletrafficSymposium PGTS, 2004. 15[37] J. Edmonds and R.M. Karp. Theoretical improvements in algorithmicefficiency for network flow problems. J. ACM, 19(2):248–264, 1972. 47[38] A. Elwalid, C. Jin, S. H. Low, and I. Widjaja. MATE: MPLS adaptivetraffic engineering. In INFOCOM, pages 1300–1309, 2001. 16[39] S. Cerav Erbas and C. Erbas. A multiobjective off-line routing modelfor MPLS networks. In Proc. of the 18th International Teletraffic Congress,2003. 1687
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AcknowledgementsThis dissertation w
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AbstractThis dissertation investiga
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CONTENTS7.4 The Real ISP’s Italia
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LIST OF FIGURES7.3 Default metrics
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1.2 Objectives of the dissertation2
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Chapter 2Basic knowledgeThis chapte
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2.1 Understanding IS-IS• A = sour
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2.2 Understanding MPLS Technology2.
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2.2 Understanding MPLS TechnologyTh
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2.4 Restoration SchemesHowever, fai
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3.1 Optimization of IS-IS/OSPF rout
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3.3 Survivability problemhas been d
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4.1 Heuristic Algorithmsmeta-heuris
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4.1 Heuristic Algorithmsto keep a c
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4.1 Heuristic Algorithmsprobability
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4.3 Linear Programming ApproachExam
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5.2 Traffic monitoring• 60% of th
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5.2 Traffic monitoringExporting the
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5.2 Traffic monitoringare included
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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
Page 57 and 58: 6.2 Graphs and Network FlowsWhere B
Page 59 and 60: 6.2 Graphs and Network Flows6.2.3 T
Page 61 and 62: 6.3 Multicommodity Flow Problemscom
Page 63 and 64: 6.4 LP modelsThe second layer intro
Page 65 and 66: 6.4 LP modelsEquation 6.32 is the o
Page 67 and 68: 6.4 LP modelsmalized as follows:z =
Page 69 and 70: 6.4 LP models(a) Rerouting flow p
Page 71 and 72: 6.4 LP models(a) 1 st solution(b) 2
Page 73 and 74: 6.4 LP models∑f∈Fx f,lij⎛⎝i
Page 75 and 76: 6.4 LP modelsterm is the MPLS part
Page 77 and 78: 7.2 The highly meshed network7.2 Th
Page 79 and 80: 7.3 The IBCN European network7.3 Th
Page 81 and 82: 7.3 The IBCN European networkmaximu
Page 83 and 84: 7.3 The IBCN European networkSurviv
Page 85 and 86: 7.3 The IBCN European networkFigure
Page 87 and 88: 7.4 The Real ISP’s Italian Backbo
Page 93 and 94: 7.5 Conclusion and validation of re
Page 95 and 96: optimal configuration of network pa
Page 97: REFERENCES[14] Jones Lustig Format
Page 101: REFERENCES[61] A. Nucci, B. Schroed
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