Davide Cherubini - PhD Thesis - UniCA Eprints

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6.4 LP modelsModel for GRPz = min(u max ) (6.57)∑is f · x f ij +∑p h ≤ u max · c ij ∀(i, j) ∈ A (6.58)f∈F∑h∈P(f)h:(i,j)∈P(h)p h = d f − is f ∀f ∈ F (6.59)p h ≥ 0 ∀h ∈ P(f) (6.60)is f ∈ [0, d f ] ∀f ∈ F (6.61)Survivability ConstraintsIn this model the 100% degree of survivability is realized by means of explicitLSPs, found as solution of the model, and implemented in the router configurationas backup secondary paths. Each backup path, represented by a new variable q h ,is common for all the working primary paths obtained for each commodity f.The recovery scheme used is the path restoration technique.As can be seen from the equations 6.62, 6.63, 6.64, 6.65, and 6.66 the survivabilityconstraints are quite complex in this case and the model requires particularsolving techniques such as Column Generation [62].∑h∈P(f), l∈P(h)∑f∈Fp h ≤∑h∈P(f), l∉P(h)is f · x f,lij + ∑h:(i,j)∈P(h), l∉P(h)q h ∀f ∈ F, ∀l ∈ A (6.62)(p h + q h ) ≤ u max · c ij ∀(i, j), l ∈ A (6.63)q h ≤ d f(h) · y h ∀h ∈ P(f) (6.64)y h ∈ {0, 1} ∀h ∈ P(f) (6.65)∑y h ≤ 1 ∀f ∈ F (6.66)h∈P(f)Equation 6.62 states that the flow to be rerouted (p h ) has to be smaller thanthe capacity of the backup path (q h ).Constraint 6.63 represents the flow conservation equation for arc (i, j). The firstterm is the IS-IS traffic flowing along the arc when the edge l fails, while the second63
6.4 LP modelsterm is the MPLS part composed by the primary working path (unaffected bythe failure) and the backup path.Following constraints 6.64 and 6.65 state if the backup path is used or not.64
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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
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
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: 6.4 LP models∑f∈Fx f,lij⎛⎝i
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 and 98: REFERENCES[14] Jones Lustig Format
Page 99 and 100: REFERENCES[40] A. V. Fiacco and G.
Page 101: REFERENCES[61] A. Nucci, B. Schroed
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Davide Cherubini - PhD Thesis - UniCA Eprints

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