The optimal routing problem in multicomputer networks: an evolutionary approach

Optimal resource allocation is an important issue in computer network administration. One of these problems involves finding an optimal route to transport certain traffic from a source node to a destination node. For messages to get from the sender to the receiver it is necessary to make a number of hops choosing, at each of the intermediate nodes, an outgoing line to use. Selection of an outgoing link can depend on amount of traffic, type of link or other criteria based on the associated cost to each line. The total transportation cost through any of the possible routes is to be minimised. Instead of facing the problem in a step by step decision making fashion, a global approach based on long term averages can be successfully used when network traffic is not extremely dynamic. Given the number of nodes in the network and the interconnection topology this later approach leads to a highly combinatorial problem. Evolutionary Algorithms behave efficiently in searching optimal or near optimal solutions in a wide range of hard combinatorial problems. Moreover, when using an evolutionary approach, instead of a single optimal solution a set of near optimal solutions is provided. This property allows us to provide timely acceptable solutions when the network interconnectivity changes over time. This paper describes a genetic algorithm using a sort of edge crossover, operating on variable length chromosomes. Also a macro-mutation operator is introduced by replacing an entire chromosome to avoid costly repair mechanisms. A report on experiments and results contrasted against conventional approaches is also included.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

The optimal routing problem in multicomputer networks: an evolutionary approach

Optimal resource allocation is an important issue in computer network administration. One of these problems involves finding an optimal route to transport certain traffic from a source node to a destination node. For messages to get from the sender to the receiver it is necessary to make a number of hops choosing, at each of the intermediate nodes, an outgoing line to use. Selection of an outgoing link can depend on amount of traffic, type of link or other criteria based on the associated cost to each line. The total transportation cost through any of the possible routes is to be minimised. Instead of facing the problem in a step by step decision making fashion, a global approach based on long term averages can be successfully used when network traffic is not extremely dynamic. Given the number of nodes in the network and the interconnection topology this later approach leads to a highly combinatorial problem. Evolutionary Algorithms behave efficiently in searching optimal or near optimal solutions in a wide range of hard combinatorial problems. Moreover, when using an evolutionary approach, instead of a single optimal solution a set of near optimal solutions is provided. This property allows us to provide timely acceptable solutions when the network interconnectivity changes over time. This paper describes a genetic algorithm using a sort of edge crossover, operating on variable length chromosomes. Also a macro-mutation operator is introduced by replacing an entire chromosome to avoid costly repair mechanisms. A report on experiments and results contrasted against conventional approaches is also included.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

BANDWIDTH EFFICIENT FORMATION OF BROADCAST NETWORK WITH MULTIPLE DESCRIPTION CODING

In this paper, we consider the delay and fault-tolerance of data  broadcasting in  Internet of Things (IoT) networks,  in  which  nodes  form  a network  topology to deliver  live data  from  a source  to the  end receivers. We first consider to build a Small Height Tree which gives an overlay  with small expected  end-to-end  delay. The end-to-end delay and the fault-tolerance can be improved by  adopting  appropriate topology  for  the  overlay  according  to the  characteristics of providers.   Efficient and fault-tolerant in service level agreement (SLA) guaranteed services can hardly be achieved solely by tree or mesh. By multiple-path data  delivery with  multiple  description coding,  service  operators can  use the scheme  to predict  the  amount  of resources to be acquired,  and hence the cost, from  the network

Optimización del costo de enlaces en una red

El proyecto de fin de carrera (PFC) desarrolla un software para encontrar los enlaces óptimos para interconectar los nodos de una red determinada, aplicando una técnica de Inteligencia Artificial como son los Algoritmos Genéticos los cuales forman parte de la rama del Soft Computing. El software esta formado por los siguientes módulos: • Modulo para cargar y leer los datos introducidos por el usuario que describen el problema o su equivalente generador de problemas mediante el uso de datos aleatorios • Algoritmo Genético que determine las terminales o dispositivos de telecomunicación que conformen el núcleo Backbone de una red. Mediante la formación de grupos de nodos, en los cuales exista simetría en el ancho de banda requerido • Algoritmo Genético que determine el costo mínimo para interconectar las terminales o dispositivos principales cumpliendo con los requerimientos que cada terminal especifica • Interfaz grafica para presentar los resultados, en la cual se puedan realizar ajustes a la solución aproximada • Modulo para generar los reportes correspondientes al cálculo de la solución final • Modulo de pruebas para comprobar la capacidad del software Siendo los más importantes, los Algoritmos Genéticos desarrollados para determinar el núcleo Backbone de la red, y los enlaces necesarios para interconectar los nodos con un costo mínimo. Las pruebas realizadas al funcionamiento del software nos indican el rendimiento y el tiempo requerido para encontrar una solución. El software es programado en MATLAB, de manera que puede ejecutarse tanto en plataformas Windows como Unix. La planificación del PFC esta pensado para ser utilizada por diseñadores de redes, analistas de redes, administradores de redes y como un programa ejemplo con fines académicos.Ingeniería en InformáticaInformatika Ingeniaritz

Survivable network design of all-optical network.

Kwok-Shing Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 69-71).Abstracts in English and Chinese.List of Figures --- p.viList of Tables --- p.viiChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Objectives --- p.6Chapter 1.3 --- Outline of Thesis --- p.8Chapter Chapter 2 --- The Spare Capacity Planning Problem --- p.9Chapter 2.1 --- Mathematical Model of the Spare Capacity Planning Problem --- p.12Chapter 2.1.1 --- Variable Definitions --- p.12Chapter 2.1.2 --- Objective Function and Constraints --- p.15Chapter 2.1.3 --- Complexity --- p.17Chapter 2.2 --- Greedy Algorithm - Spare Capacity Allocation and Planning Estimator (SCAPE) --- p.19Chapter 2.2.1 --- Working Principle of SCAPE --- p.20Chapter 2.2.2 --- Implementation of SCAPE --- p.22Chapter 2.2.3 --- Improved SCAPE --- p.23Chapter 2.3 --- Experimental Results and Discussion --- p.27Chapter 2.3.1 --- Experimental Platform --- p.27Chapter 2.3.2 --- Experiment about Accuracy of SCAPE --- p.27Chapter 2.3.3 --- Experiment about Minimization of Network Spare Capacity --- p.30Chapter 2.3.4 --- Experiment about Minimization of Network Spare Cost --- p.35Chapter 2.4 --- Conclusions --- p.38Chapter Chapter 3 --- Survivable All-Optical Network Design Problem --- p.39Chapter 3.1 --- Mathematical Model of the Survivable Network Design Problem --- p.42Chapter 3.2 --- Optimization Algorithms for Survivable Network Design Problem --- p.44Chapter 3.2.1 --- Modified Drop Algorithm (MDA) --- p.45Chapter 3.2.1.1 --- Drop Algorithm Introduction --- p.45Chapter 3.2.1.2 --- Network Design with MDA --- p.45Chapter 3.2.2 --- Genetic Algorithm --- p.47Chapter 3.2.2.1 --- Genetic Algorithm Introduction --- p.47Chapter 3.2.2.2 --- Network Design with GA --- p.48Chapter 3.2.3 --- Complexity of MDA and GA --- p.51Chapter 3.3 --- Experimental Results and Discussion --- p.52Chapter 3.3.1 --- Experimental Platform --- p.52Chapter 3.3.2 --- Experiment about Accuracy of MDA and GA --- p.52Chapter 3.3.3 --- Experiment about Principle of Survivable Network Design --- p.55Chapter 3.3.4 --- Experiment about Performance of MDA and GA --- p.58Chapter 3.4 --- Conclusions --- p.62Chapter Chapter 4 --- Conclusions and Future Work --- p.63Appendix A The Interference Heuristic for the path restoration scheme --- p.66Bibliography --- p.69Publications --- p.7

Algoritmos genéticos y su aplicación en optimización de redes

El presente trabajo realiza un análisis de los algoritmos genéticos que se diseñan para resolver problemas de optimización, que involucran no sólo funciones objetivo continuas y derivables sino aplicados a funciones con puntos de discontinuidad o de no derivabilidad. Asimismo se aplican a problemas de secuenciación en donde el espacio de soluciones está determinado por un conjunto de secuencias una de las cuales es la óptima, presente en muchos problemas de optimización en redes. En este tipo de problemas están presente las permutaciones y su representación intrínseca ha constituido un reto para los algoritmos genéticos. Se presenta un análisis de diferentes representaciones de los cromosomas que pueden ser utilizados en la resolución de los distintos problemas y del funcionamiento de los AG en los distintos casos, representaciones y parámetros que los gobiernan. Se seleccionan algunos ejemplos de aplicaciones de algoritmos genéticos en redes en los cuales se distinguen diferentes tipos de problemas y de aportes en los AG en cada ejemplo. Se hace hincapié en los operadores genéticos seleccionando para cada caso los más apropiados. Posteriormente se encara el diseño e implementación de un AG, utilizando el problema del viajante para un testeo preliminar de los AG y finalmente se aplica este diseño en uno de los ejemplos seleccionados. Conjuntamente se implementan algunas técnicas clásicas para contrastar los resultados. Finalmente se realiza una interpretación de los resultados justificando la exploración de estas técnicas como una alternativa válida en problemas de optimización de redes de datos, analizando las ventajas y desventajas de estos métodos frente a técnicas clásicas.Tesis digitalizada en SEDICI gracias a la colaboración de la Biblioteca de la Facultad de Informática.Facultad de Ciencias Exacta

Netted radar modelling, design and optimisation

Networks of phased array radars are able to provide better counter stealth target detection and classification. Each radar sensor/node generates information which requires transmission to a central control authority who is able to evaluate the information. This requires a communications network to be established to allow transmission of information to and from any node. Each radar node is limited by range and degree and relies on the formation of a multi-hop network to facilitate this transmission. This thesis investigates a method whereby the radar beam itself is used in the formation of a multi-hop network. The phased array's multifunctional nature allows rapid switching between communications and radar function. A complete model of how the communication system could operate is included in this thesis. In order to simulate radar function and network communication, a custom built simulation platform was used. Two different approaches to the formation of the radar networks are derived, one involving a global design method using the Strength Pareto Evolutionary Algorithm (SPEA) and another using a local method specifically tailored to radar network formation, Distributed Algorithm for Radar Topology Control (DARTC). Both of these techniques can be used in the formation of radar networks. The thesis goes further to suggest modifications to the model itself that could result in improved radar network communication performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Netted radar modelling, design and optimisation

Networks of phased array radars are able to provide better counter stealth target detection and classification. Each radar sensor/node generates information which requires transmission to a central control authority who is able to evaluate the information. This requires a communications network to be established to allow transmission of information to and from any node. Each radar node is limited by range and degree and relies on the formation of a multi-hop network to facilitate this transmission. This thesis investigates a method whereby the radar beam itself is used in the formation of a multi-hop network. The phased array's multifunctional nature allows rapid switching between communications and radar function. A complete model of how the communication system could operate is included in this thesis. In order to simulate radar function and network communication, a custom built simulation platform was used. Two different approaches to the formation of the radar networks are derived, one involving a global design method using the Strength Pareto Evolutionary Algorithm (SPEA) and another using a local method specifically tailored to radar network formation, Distributed Algorithm for Radar Topology Control (DARTC). Both of these techniques can be used in the formation of radar networks. The thesis goes further to suggest modifications to the model itself that could result in improved radar network communication performance