Chu Pok Nang. / Thesis submitted on: October 2002. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Chapter 1. --- Introduction / Group Technology --- p.6 / Purposes of Research --- p.10 / The Outline of this Thesis --- p.13 / Chapter 2. --- Literature Review / Algorithms for Group Technology --- p.14 / Hierarchical Clustering Approaches --- p.17 / Sorting Based Approaches --- p.18 / Heuristic Exchange Approaches --- p.19 / Seed Based Approaches --- p.20 / Simulated Annealing Approaches --- p.20 / Tabu Search Approaches --- p.21 / Genetic Algorithm Approaches --- p.21 / Neural Network Approaches --- p.22 / Cluster Identification Approaches --- p.22 / Chapter 3. --- The Group Technology Problem / Representing a Manufacturing System --- p.25 / Machine-Part Incidence Matrix --- p.26 / Chapter 4. --- The Improved Cluster Identification Algorithm / Cluster Identification --- p.34 / Formulation --- p.35 / Branch-and-Bound Method --- p.37 / Original Cluster Identification Algorithm --- p.39 / Branching Rule --- p.44 / Chapter 5. --- Computational Studies / Plans for Comparative Studies --- p.49 / Comparison with Existing Cluster Identification Approaches --- p.51 / Solutions to Some Well-known Problems --- p.53 / Comparison with an Optimal Method --- p.60 / Chapter 6. --- Conclusion --- p.63 / Reference --- p.69

No description available.

Law, Shu Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 39-40). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Basic concept of clustering --- p.1 / Chapter 1.2 --- Main problems --- p.2 / Chapter 1.3 --- Review --- p.3 / Chapter 1.4 --- Newly proposed method --- p.7 / Chapter 1.5 --- Summary --- p.7 / Chapter 2 --- k-means clustering --- p.9 / Chapter 2.1 --- Algorithm of k-means clustering --- p.9 / Chapter 2.2 --- Selecting k in k-mcans clustering --- p.11 / Chapter 2.3 --- Disadvantages of k-means clustering --- p.12 / Chapter 3 --- Methodology and Algorithm --- p.14 / Chapter 3.1 --- Methodology and Algorithm --- p.14 / Chapter 3.2 --- Illustrative Example --- p.20 / Chapter 4 --- Simulation Study --- p.25 / Chapter 4.1 --- Simulation Plan --- p.25 / Chapter 4.2 --- Simulation Details --- p.27 / Chapter 4.3 --- Simulation Result --- p.30 / Chapter 4.4 --- Summary --- p.34 / Chapter 5 --- Conclusion and Further research --- p.36 / Bibliography --- p.38

No description available.

No description available.

A cluster-based router is a new router architecture that is composed of a cluster of commodity processing nodes interconnected by a high-speed and low-latency network. It inherits packet processing extensibility from the software router, and forwarding performance scalability from clustering. In this thesis, we describe a prototype cluster-based router, including the design of the cluster-based router architecture and the addressing of critical issues such as the design of a highly efficient communication layer, reduction of operating system overheads, buffer recycling and packet packing. By experimental evaluation, we expose its forwarding capacity scalability and latency variance. We also evaluate and analyze the potential hardware bottlenecks of its commodity processing nodes, and present the correlation between the reception and transmission capabilities of an individual port as well as ports on the same bus. We propose an adaptive scheduling mechanism based on system state information to manage the adverse effect of this correlation on the router performance. We also investigate internal congestion in the cluster-based router. To manage the internal congestion, we propose two backward explicit congestion notification schemes: a novel queue scheduling method and an optimal utility-based scheme. We show the effectiveness of these schemes either by ns-3 simulation, experimental evaluation, or both. We also analyze the stability of the optimal utility-based BECN internal congestion control scheme through theoretical proof, simulation and experimental evaluation.

Thesis (M.Sc.)--York University, 2003. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 112-121) and index. Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99310

This dissertation presents the configuration and capacity management of manufacturing cells using cluster analysis. A heuristic based on cluster analysis is developed to solve cell formation in cellular manufacturing systems (CMS). The clustering heuristic is applied for cell formation considering processing requirement (CFOPR) as well as various manufacturing factors (CFVMF). The proposed clustering heuristic is developed by employing a new solving structure incorporating hierarchical and non-hierarchical clustering methods. A new similarity measure is constructed by modifying the Jarccard similarity and a new assignment algorithm is proposed by employing the new pairwise exchange method. In CFOPR, the clustering heuristic is modified by adding a feedback step and more exact allocation rules. Grouping efficacy is employed as a measure to evaluate solutions obtained from the heuristic. The clustering heuristic for CFOPR was evaluated on 23 test problems taken from the literature in order to compare with other approaches and produced the best solution in 18 out of 23 and the second best in the remaining problems. These solutions were obtained in a considerably short time and even the largest test problem was solved in around one and a half seconds. In CFVMF, the machine capacity was first ensured, and then manufacturing cells were configured to minimize intercellular movements. In order to ensure the machine capacity, the duplication of machines and the split of operations are allowed and operations are assigned into duplicated machines by the largest-first rule. The clustering heuristic for CFVMF proposes a new similarity measure incorporating processing requirement, material flow and machine workload and a new machine-part matrix representing material flow and processing time assigned to multiple identical machines. Also, setup time, which has not been clearly addressed in existing research, is discussed in the solving procedure. The clustering heuristic for CFVMF employs two evaluation measures such as the number of intercellular movements and grouping efficacy. In two test problems taken from the literature, the heuristic for CFVMF produced the same results, but the trade-off problem between the two evaluation measures is proposed to consider the goodness of grouping.

Diferentes estudios realizados sobre el grado de utilización de los recursos de cómputo (CPU y memoria) en una red de PCs (cluster/NOW), han puesto de manifiesto que un elevado porcentaje de los mismos están infrautilizados. La posibilidad de utilizar esta potencia de cálculo para la ejecución de aplicaciones distribuidas con un rendimiento equivalente a un MPP, sin perturbar el trabajo del usuario local de cada workstation, ha sido objeto de estudio en este trabajo. Este doble objetivo puede ser alcanzado mediante el uso de técnicas de tiempo compartido. Un problema intrínseco de las técnicas de tiempo compartido es el modo de garantizar la coplanificación de aquellas tareas distribuidas que se comunican entre si; siendo este problema conocido como coscheduling. El principal objetivo del coscheduling es minimizar el tiempo de espera de las tareas distribuidas ante los eventos de comunicación y sincronización. El coscheduling puede ser alcanzado mediante la asignación de la CPU acorde con la ocurrencia de ciertos eventos locales, tradicionalmente de comunicación. De este modo, todas las tareas de una misma aplicación distribuida podrán progresar coordinadamente a lo largo del cluster.Este nuevo marco de trabajo impone, en nuestro modo de ver, un replanteamiento del problema clásico del coscheduling de aplicaciones distribuidas. El fin del coscheduling no solamente se debe restringir a decidir cuándo deben ser asignados los recursos de cómputo a las aplicaciones distribuidas, finalidad de las técnicas de coscheduling tradicionales, si no también cuántos recursos deben asignarse a cada aplicación. Este doble propósito nos ha llevado a desarrollar una nueva propuesta de coscheduling, denominada CoScheduling Cooperativo (CSC), orientada a la coordinación de múltiples aplicaciones paralelas en un entorno cluster no dedicado.CSC, a diferencia de las propuestas de coscheduling tradicionales, gestiona los recursos de cómputo de cada nodo, tanto en función de la ocurrencia de determinados eventos locales (de memoria, de CPU, de comunicación y de actividad del usuario local), como de la recepción de aquellos eventos ocurridos en nodos remotos y que han modificado los recursos asociados a los procesos cooperantes. El análisis de estos eventos permite a CSC adaptar los recursos de cómputo del cluster a las necesidades de ambos tipos de usuarios; el usuario local, caracterizado por unos elevados requerimientos de interactividad, y el usuario de las aplicaciones paralelas, en el cual priman los requerimientos de cómputo y de comunicación. De este modo, CSC puede gestionar la ejecución de múltiples aplicaciones paralelas simultáneamente, hecho que conlleva una mejor eficiencia en el uso de los recursos de cómputo disponibles.CSC ha sido implementado en un entorno Linux-PVM. Esta implementación ha permitido evaluar su rendimiento, con respecto a las políticas de coscheduling tradicionales, tanto en un entorno cluster controlado como en un entorno cluster productivo. La experimentación realizada ha mostrado como CSC obtiene unos resultados globales mejores que el resto de políticas evaluadas, tanto en lo que respecta al rendimiento de las aplicaciones del usuario local como de las aplicaciones distribuidas. Los resultados obtenidos por CSC demuestran que el desarrollo de políticas de planificación a corto plazo orientadas a entornos no dedicados permiten explotar, de una manera eficiente, todos aquellos recursos de cómputo disponibles, obteniendo unas métricas de speedup satisfactorias (en muchos casos superior a la mitad del número de tareas de la aplicación) y provocando un overhead inapreciable para el usuario local.

No description available.