In this thesis, we propose a high level design for connectivity visualization of OLSRbased MANET topology based on local topology databases available in an OLSR node. Two different scenarios are considered: a central (full view) topology from a command and control location, or a nodal (partial) view from an ad-hoc node. A simulation-based analysis is conducted to calculate total number of active links at a particular time in full and nodal topology views. Also the error rate of network topology discovery based on total undiscovered link both mobile and static scenario is considered and reported. We also come up with an analytical model to analyse the network bandwidth and overhead of using TC, HELLO and custom NIM message to evaluate the performance of centralized visualization to build full map of the network with respect to situational awareness system. This thesis also presents a multi-node, 2-dimensional, distributed technique for coarse (approximate) localization of the nodes in a tactical mobile ad hoc network. The objective of this work is to provide coarse localization information based on layer-3 connectivity information and a few anchor nodes or landmarks, and without using traditional methods such as signal strength, Time of Arrival (ToA) or distance information. We propose a localization algorithm based on a Force-directed method that will allow us to estimate the approximate location of each node based on network topology information from a local OLSR database. We assume the majority of nodes are not equipped with GPS and thus do not have their exact location information. In our proposed approach we make use of the possible existence of known landmarks as reference points to enhance the accuracy of localization. / UOIT

It¡¦s the age of network now. More and more people use the network and network management becomes important. The ISO sets the network management model: accounting management, configuration management, performance management, fault management and security management. Network topology plays an important role of them. Network topology can help network manager to manage network devices efficiently and to handle some problems. So, topology discovery is an important technology that can help us to find out the network topology. Although there are many network management tools having topology discovery function, they are commercial secret and usually need pay. Because of network topology is important of network management and commercial network management software isn¡¦t free, we want to study topology discovery to find the suitable algorithm of topology discovery which can find out network layer topology and data link layer topology. Then we will modify the algorithm to help us to implement the system of topology discovery. This system needs minor factors and less influence on network. It can discover topology efficiently.

Software-De_ned Networking (SDN) proposes a new network architecture inwhich the control plane and forwarding plane are decoupled. SDN can improvenetwork e_ciency and ease of management through the centralization of the controland policy decisions. However, SDN deployments are currently limited todata-center and experimental environments. This thesis surveys the deploymentof SDN from the perspective of a telecommunication network operator. We discussthe strategies which enable the operator to migrate to a network in whichboth SDN and legacy devices interoperate. As a synthesis of existing technologiesand protocols, we formulate an automated process for the bootstrapping of newlydeployed forwarding devices. Furthermore, we review solutions for programmingthe forwarding devices and for performing topology discovery. The functionalcorrectness of the proposed bootstrapping process is evaluated in an emulatedenvironment.

Knowledge of a network’s entities and the physical connections between them, a network’s physical topology, can be useful in a variety of network scenarios and applications. Administrators can use topology information for fault- finding, inventorying and network planning. Topology information can also be used during protocol and routing algorithm development, for performance prediction and as a basis for accurate network simulations. Specifically, from a network security perspective, threat detection, network monitoring, network access control and forensic investigations can benefit from accurate network topology information. The dynamic nature of large networks has led to the development of various automatic topology discovery techniques, but these techniques have mainly focused on cooperative network environments where network elements can be queried for topology related information. The primary objective of this study is to develop techniques for discovering the physical topology of an Ethernet network without the assistance of the network’s elements. This dissertation describes the experiments performed and the techniques developed in order to identify network nodes and the connections between these nodes. The product of the investigation was the formulation of an algorithm and heuristic that, in combination with measurement techniques, can be used for inferring the physical topology of a target network. / Dissertation (MSc)--University of Pretoria, 2008. / Computer Science / unrestricted

No description available.

No description available.

Dans les réseaux énergétiques, la connaissance des équipements, leurs emplacements et leursfonctions sont les prérequis à l’exploitation de l’infrastucture. En effet, tout opérateur disposed’une carte appelée schéma synoptique indiquant les connexions entre les équipements. À partirde cette carte, sont prises des décisions pour un fonctionnement optimal du réseau.Ce schéma synoptique peut être érronné parce que des opérations de maintenance sur le réseaun’auraient pas été retranscrites ou mal saisies. Et cela peut entrainer des coûts supplémentairesd’exploitation du réseau énergetique.Nous considérons le réseau électrique d’un Datacenter. Ce réseau est composé d’une topologiephysique modélisée par un DAG sans circuit et de mesures électriques sur ces arcs. La particularitéde ce réseau est que les mesures contiennent des erreurs et cette topologie est inconnue c’est-à-direles arcs sont connus mais les extrémités des arcs sont inconnues. Dans le cas où ces mesuressont correctes alors la corrélation des arcs induit la matrice d’adjacence du line-graphe du graphenon-orienté sous-jacent de notre DAG. Un line-graphe est un graphe dans lequel chaque sommet etson voisinage peuvent être partitionnés par une ou deux cliques et que chaque arête est couvertepar une clique. Cependant, avec la présence des erreurs de mesures, nous avons un graphe avecdes arêtes en plus ou en moins qui n’est pas nécessairement un line-graphe. Si ce graphe est unline-graphe alors il n’est pas le line-graphe de notre DAG. Notre problème est de découvrir cettetopologie en se basant sur ces mesures électriques.Nous débutons par une étude bibliographique des corrélations de mesures possibles afin dedéterminer celle qui est pertinente pour notre problème. Ensuite nous proposons deux algorithmespour résoudre ce problème. Le premier algorithme est l’algorithme de couverture et il déterminel’ensemble des cliques qui couvre chaque sommet de notre graphe. Le second algorithme estl’algorithme de correction. Il ajoute ou supprime des arêtes au voisinage d’un sommet non couvertde telle sorte que son voisinage soit partitionné en une ou deux cliques. Enfin, nous évaluons lesperformances de nos algorithmes en vérifiant le nombre d’arêtes corrigées et la capacité à retournerle graphe le plus proche du line-graphe de notre DAG. / In energy network, the knowledge of equipments, their locations and their functions are theimportant information for the distributor service operator. In fact, each operator has a networkplan often named synoptic schema. That schema shows the interconnexion between equipments inthe network. From this schema, some management decisions have taken for ensuring an optimalperformance of a network.Sometimes, a synoptic schema has some mistakes because the maintenance operations, such aschanged the connexion between equipments or replaced equipments, have not been updated orhave been written with errors. And these mistakes increase exploitation cost in the energy network.We consider an electric network of a datacenter. This network consists of physical topologymodelised by a DAG without circuit and measurements are on the edges of a DAG. The mainpoint of the network is that measurements are some mistakes and the topology is unknown i.ewe know edges but the nodes of edges are unknown. When measurements are correct then thecorrelations between pairwise edges provide the adjacency matrix of the linegraph of undirectedgraph of the DAG. A linegraph is a graph in which each node and the neighbor are partitionnedby one or deux cliques. However, with the mistakes in measurements, the obtained graph is nota linegraph because it contains more or less edges. If the obtained graph is a linegraph then it isa linegraph of the other DAG. Our problem is to discovery the topology of the DAG with somemistakes in measurements.We start by the state of art in the measurement correlations in order to choose the good methodfor our problem. Then, we propose two algorithms to resolve our problem. The first algorithmis the cover algorithm and it returns the set of cliques in the graph. The second algorithm is acorrection algorithm which adds or deletes edges in the graph for getting a nearest linegraph ofthe DAG. In the last, we evaluate the performances of the algorithms by checking the number ofedges corrected and the ability to return a nearest linegraph of the DAG.

Landmark-based architecture has been commonly adopted in the networking community as a mechanism to measure and characterize a host's location on the Internet. In most existing landmark based approaches, end hosts use the distance measurements to a common, fixed set of landmarks to derive an estimated location on the Internet. This paper investigates whether it is possible for participating peer nodes in an overlay network to collaboratively construct an accurate geometric model of its topology in a completely decentralized peer-to-peer fashion, without using a fixed set of landmarks. We call such a peer-to-peer approach in topology discovery and modeling using landmarks PALM (Peers As LandMarks). We evaluate the performance characteristics of such a decentralized coordinates-based approach under several factors, including dimensionality of the geometric space, peer distance distribution, and the number of peer-to-peer distance measurements used. We evaluate two PALM-based schemes: RAND-PALM and ISLAND. In RAND-PALM, a peer node randomly selects from existing peer nodes as its landmarks. In ISLAND (Intelligent Selection of Landmarks), each peer node selects its landmarks by exploiting the topological information derived based on existing peer nodes' coordinates values. / Singapore-MIT Alliance (SMA)

Modern heterogeneous networks present a great challenge for network operators and engineers from a management and configuration perspective. The Tail-f Systems’ Network Control System (NCS) is a network management framework that addresses these challenges. NCS offers centralized network configuration management functionality, along with providing options for extending the framework with additional features. The devices managed by NCS are stored in its Configuration Database (CDB). However, currently there is no mechanism for automatically adding network devices to the configuration of NCS, thus each device’s management parameters have to be entered manually. The goal of this master’s thesis project is to develop a software module for NCS that simplifies the process of initial NCS configuration by allowing NCS to automatically add network devices to the NCS CDB. Apart from developing the software module for discovery, this project aims to summarize existing methods and to develop new methods for automated discovery of network devices with the main focus on differentiating between different types of devices. A credential-based device discovery method was developed and utilized to make advantage of known credentials to access devices, which allows for more precise discovery compared to some other existing methods. The selected methods were implemented as a component of NCS to provide device discovery functionality. Another focus of this master’s thesis project was the development of an approach to network topology discovery and its representation. The aim is to provide both a logical Internet Protocol (IP) network topology and a physical topology of device interconnections. The result is that we are able to automatically discover and store the topology representation as a data structure, and subsequently generate a visualization of the network topology. / Moderna heterogena nätverk utgör en stor utmaning för operatörer och ingenjörer att hantera och konfigurera. Tail-f Systems NCS produkt är ett ramverk för nätverks konfiguration som addresserar dessa utmaningar. NCS är ett centraliserat nätverks konfigurations verktyg. NCS är användbart som det är, men kan också byggas ut av användaren med ytterligare funktioner. De enheter som hanteras med NCS lagras i konfigurationsdatabasen (CDB). För närvarande finns det ingen automatiserad mekanism för att addera nätverksenheter till NCS, och varje enhets parametrar måste fyllas i manuellt. Detta examensarbetes mål är att utveckla en mjukvarumodul för NCS som förenklar NCS konfiguration genom att automatiskt lägga nätverksenheter till NCS CDB. Förutom att utveckla programvara för enhetsidentifiering, syftar detta projekt till att sammanfatta befintliga metoder och utveckla nya metoder för automatiserad nätverksenhetsidentifiering med huvudfokus på att skilja mellan olika typer av enheter. En metod baserad på förkonfigurerade autenticeringsuppgifter har utvecklats och den används för att precist kunna identifiera olika typer av nätverkselement. De valda metoderna har implementerats som en optionell modul till NCS som erbjuder enhetsidentifieringsfunktionalitet. Ytterligare ett fokus för detta examensarbete har varit att utveckla metoder för identifieraing av nätverkstopologin, och modeller för hur topologin ska representeras.  Vi har syftat till att identifiera både den logiska IP nätverkstopologin (L3) och den fysiska topologin av sammankopplade enheter (L2). Den viktigaste uppgiften har varit att identifiera och lagra topologi representation som en datastruktur, och dessutom generera en visualisering av nätverkstopologin.