Davide Cherubini - PhD Thesis - UniCA Eprints

More documents

Recommendations

Info

4.2 Deterministic Algorithms• If a cycle is detected, that is, if an ant is forced to return to an alreadyvisited node, the cycle’s nodes are popped from the ant’s stack and all thememory about them is destroyed.• When the destination node d is reached, the agent becomes a different agent(backward ant) transferring to it all the contents of its memory.• The backward ant takes, in the opposite direction, the same path as thatof its corresponding forward ant. At each traversed node along the path itpops its stack to know the next hop node.• At each node reached, the backward ant updates two data structures heldand updated by every node, that are a routing table, organized as in vectordistancealgorithms, and a list representing a “memory” of the network asseen by the reached node in probability terms.The update of the routing table happens using the trip time experienced by theforward ant, that gives a clear indication about the goodness of the followed routefrom a physical point of view (number of hops, transmission capacity of the usedlinks, processing speed of the crossed nodes) and from a traffic congestion pointof view.4.2 Deterministic AlgorithmsIn deterministic models good decisions bring about good outcomes. Given a particularinput, it will always produce the same correct output, and the underlyingmachine will always pass through the same sequence of states. Therefore, theoutcome is deterministic (e.g. risk-free).Deterministic algorithms are by far the most studied and familiar kind of algorithm,as well as one of the most practical, since they can be run on real machinesefficiently.One simple model for deterministic algorithms is the mathematical function;just as a function always produces the same output given a certain input. Thedifference is that algorithms describe precisely how the output is obtained fromthe input, whereas abstract functions may be defined implicitly.25
4.3 Linear Programming ApproachExamples of particular abstract machines which are deterministic include thedeterministic Turing machine and deterministic finite automaton.4.3 Linear Programming ApproachIn the present work Linear Programming (LP) optimization methods have beenused to perform the optimization process. The in-depth investigation of the LPtechniques is presented in Chapter 6.26
Page 4 and 5: AcknowledgementsThis dissertation w
Page 6 and 7: AbstractThis dissertation investiga
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: 4.1 Heuristic Algorithmsprobability
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 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 89 and 90:
Page 91 and 92:
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
show all

Davide Cherubini - PhD Thesis - UniCA Eprints

Create successful ePaper yourself

Delete template?

Save as template?