[[alternative]]Designing User Location Tracking Strategies in Personal Mobile Communication Networks

計畫編號：NSC92-2213-E032-011研究期間：200308~200407研究經費：487,000[[sponsorship]]行政院國家科學委員

Improvement of the Performance of Database Access Operations in Cellular Networks, Journal of Telecommunications and Information Technology, 2012, nr 3

Reducing the traffic volume of location updating is a critical issue for tracking mobile users in a cellular network. Besides, when user x wants to communicate with user y, the location of user y must be extracted from databases. Therefore, one or more databases must be accessed for updating, recording, deleting, and searching. Thus, the most important criterion of a location tracking algorithm is to provide a small database access time. In this paper, we propose a new location tracking scheme, called Virtual Overlap Region with Forwarding Pointer (VF), and compare the number of database accesses required for updating, deleting, and searching operations for the proposed scheme and other approaches proposed for cellular networks. Our VF scheme like Overlap Region scheme reduces the updating information when a user frequently moves in boundaries of LAs. Unlike Overlap Region, the VF can reduce number of database accesses for searching users’ information

Development of Multilevel Distributed Database Architecture for Solving (GSM) Centralized Database Accessing Problem in Nigeria

Telecommunication is the basis of economic and political growth of any society. Since the advent of GSM in Nigeria, different operator have been battling with diverse challenges which erupted as a result of GSM growth and increased in number of GSM users. These challenges result in calls delay and poor quality of service which can be link to central database system. In this research work, a multilevel distributed database architecture for GSM network in Nigeria was developed and propose. It explores the use of the analytical model and numerical calling process algorithms for each location. The call arrival rate traffic to a database system is determine using mapping process ranging from state to region and finally to the center. Keywords: key Communication, Architecture, multi-level, centralized, networ

Journal of Telecommunications and Information Technology, 2012, nr 3

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Multicast outing protocols and architectures in mobile ad-hoc wireless networks

The basic philosophy of personal communication services is to provide user-to-user, location independent communication services. The emerging group communication wireless applications, such as multipoint data dissemination and multiparty conferencing tools have made the design and development of efficient multicast techniques in mobile ad-hoc networking environments a necessity and not just a desire. Multicast protocols in mobile adhoc networks have been an area of active research for the past few years. In this dissertation, protocols and architectures for supporting multicast services are proposed, analyzed and evaluated in mobile ad-hoc wireless networks. In the first chapter, the activities and recent advances are summarized in this work-in-progress area by identifying the main issues and challenges that multicast protocols are facing in mobile ad-hoc networking environments and by surveying several existing multicasting protocols. a classification of the current multicast protocols is presented, the functionality of the individual existing protocols is discussed, and a qualitative comparison of their characteristics is provided according to several distinct features and performance parameters. In the second chapter, a novel mobility-based clustering strategy that facilitates the support of multicast routing and mobility management is presented in mobile ad-hoc networks. In the proposed structure, mobile nodes are organized into nonoverlapping clusters which have adaptive variable-sizes according to their respective mobility. The mobility-based clustering (MBC) approach which is proposed uses combination of both physical and logical partitions of the network (i.e. geographic proximity and functional relation between nodes, such as mobility pattern etc.). In the third chapter, an entropy-based modeling framework for supporting and evaluating the stability is proposed in mobile ad-hoc wireless networks. The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad-hoc wireless networks and the concept of entropy. In the fourth chapter, a Mobility-based Hybrid Multicast Routing (MHMR) protocol suitable for mobile ad-hoc networks is proposed. The MHMR uses the MBC algorithm as the underlying structure. The main features that the proposed protocol introduces are the following: a) mobility based clustering and group based hierarchical structure, in order to effectively support the stability and scalability, b) group based (limited) mesh structure and forwarding tree concepts, in order to support the robustness of the mesh topologies which provides limited redundancy and the efficiency of tree forwarding simultaneously, and c) combination of proactive and reactive concepts which provide the low route acquisition delay of proactive techniques and the low overhead of reactive methods. In the fifth chapter, an architecture for supporting geomulticast services with high message delivery accuracy is presented in mobile ad-hoc wireless networks. Geomulticast is a specialized location-dependent multicasting technique, where messages are multicast to some specific user groups within a specific zone. An analytical framework which is used to evaluate the various geomulticast architectures and protocols is also developed and presented. The last chapter concludes the dissertation

Optimization of high-throughput real-time processes in physics reconstruction

La presente tesis se ha desarrollado en colaboración entre la Universidad de Sevilla y la Organización Europea para la Investigación Nuclear, CERN. El detector LHCb es uno de los cuatro grandes detectores situados en el Gran Colisionador de Hadrones, LHC. En LHCb, se colisionan partículas a altas energías para comprender la diferencia existente entre la materia y la antimateria. Debido a la cantidad ingente de datos generada por el detector, es necesario realizar un filtrado de datos en tiempo real, fundamentado en los conocimientos actuales recogidos en el Modelo Estándar de física de partículas. El filtrado, también conocido como High Level Trigger, deberá procesar un throughput de 40 Tb/s de datos, y realizar un filtrado de aproximadamente 1 000:1, reduciendo el throughput a unos 40 Gb/s de salida, que se almacenan para posterior análisis. El proceso del High Level Trigger se subdivide a su vez en dos etapas: High Level Trigger 1 (HLT1) y High Level Trigger 2 (HLT2). El HLT1 transcurre en tiempo real, y realiza una reducción de datos de aproximadamente 30:1. El HLT1 consiste en una serie de procesos software que reconstruyen lo que ha sucedido en la colisión de partículas. En la reconstrucción del HLT1 únicamente se analizan las trayectorias de las partículas producidas fruto de la colisión, en un problema conocido como reconstrucción de trazas, para dictaminar el interés de las colisiones. Por contra, el proceso HLT2 es más fino, requiriendo más tiempo en realizarse y reconstruyendo todos los subdetectores que componen LHCb. Hacia 2020, el detector LHCb, así como todos los componentes del sistema de adquisici´on de datos, serán actualizados acorde a los últimos desarrollos técnicos. Como parte del sistema de adquisición de datos, los servidores que procesan HLT1 y HLT2 también sufrirán una actualización. Al mismo tiempo, el acelerador LHC será también actualizado, de manera que la cantidad de datos generada en cada cruce de grupo de partículas aumentare en aproxidamente 5 veces la actual. Debido a las actualizaciones tanto del acelerador como del detector, se prevé que la cantidad de datos que deberá procesar el HLT en su totalidad sea unas 40 veces mayor a la actual. La previsión de la escalabilidad del software actual a 2020 subestim´ó los recursos necesarios para hacer frente al incremento en throughput. Esto produjo que se pusiera en marcha un estudio de todos los algoritmos tanto del HLT1 como del HLT2, así como una actualización del código a nuevos estándares, para mejorar su rendimiento y ser capaz de procesar la cantidad de datos esperada. En esta tesis, se exploran varios algoritmos de la reconstrucción de LHCb. El problema de reconstrucción de trazas se analiza en profundidad y se proponen nuevos algoritmos para su resolución. Ya que los problemas analizados exhiben un paralelismo masivo, estos algoritmos se implementan en lenguajes especializados para tarjetas gráficas modernas (GPUs), dada su arquitectura inherentemente paralela. En este trabajo se dise ˜nan dos algoritmos de reconstrucción de trazas. Además, se diseñan adicionalmente cuatro algoritmos de decodificación y un algoritmo de clustering, problemas también encontrados en el HLT1. Por otra parte, se diseña un algoritmo para el filtrado de Kalman, que puede ser utilizado en ambas etapas. Los algoritmos desarrollados cumplen con los requisitos esperados por la colaboración LHCb para el año 2020. Para poder ejecutar los algoritmos eficientemente en tarjetas gráficas, se desarrolla un framework especializado para GPUs, que permite la ejecución paralela de secuencias de reconstrucción en GPUs. Combinando los algoritmos desarrollados con el framework, se completa una secuencia de ejecución que asienta las bases para un HLT1 ejecutable en GPU. Durante la investigación llevada a cabo en esta tesis, y gracias a los desarrollos arriba mencionados y a la colaboración de un pequeño equipo de personas coordinado por el autor, se completa un HLT1 ejecutable en GPUs. El rendimiento obtenido en GPUs, producto de esta tesis, permite hacer frente al reto de ejecutar una secuencia de reconstrucción en tiempo real, bajo las condiciones actualizadas de LHCb previstas para 2020. As´ı mismo, se completa por primera vez para cualquier experimento del LHC un High Level Trigger que se ejecuta únicamente en GPUs. Finalmente, se detallan varias posibles configuraciones para incluir tarjetas gr´aficas en el sistema de adquisición de datos de LHCb.The current thesis has been developed in collaboration between Universidad de Sevilla and the European Organization for Nuclear Research, CERN. The LHCb detector is one of four big detectors placed alongside the Large Hadron Collider, LHC. In LHCb, particles are collided at high energies in order to understand the difference between matter and antimatter. Due to the massive quantity of data generated by the detector, it is necessary to filter data in real-time. The filtering, also known as High Level Trigger, processes a throughput of 40 Tb/s of data and performs a selection of approximately 1 000:1. The throughput is thus reduced to roughly 40 Gb/s of data output, which is then stored for posterior analysis. The High Level Trigger process is subdivided into two stages: High Level Trigger 1 (HLT1) and High Level Trigger 2 (HLT2). HLT1 occurs in real-time, and yields a reduction of data of approximately 30:1. HLT1 consists in a series of software processes that reconstruct particle collisions. The HLT1 reconstruction only analyzes the trajectories of particles produced at the collision, solving a problem known as track reconstruction, that determines whether the collision data is kept or discarded. In contrast, HLT2 is a finer process, which requires more time to execute and reconstructs all subdetectors composing LHCb. Towards 2020, the LHCb detector and all the components composing the data acquisition system will be upgraded. As part of the data acquisition system, the servers that process HLT1 and HLT2 will also be upgraded. In addition, the LHC accelerator will also be updated, increasing the data generated in every bunch crossing by roughly 5 times. Due to the accelerator and detector upgrades, the amount of data that the HLT will require to process is expected to increase by 40 times. The foreseen scalability of the software through 2020 underestimated the required resources to face the increase in data throughput. As a consequence, studies of all algorithms composing HLT1 and HLT2 and code modernizations were carried out, in order to obtain a better performance and increase the processing capability of the foreseen hardware resources in the upgrade. In this thesis, several algorithms of the LHCb recontruction are explored. The track reconstruction problem is analyzed in depth, and new algorithms are proposed. Since the analyzed problems are massively parallel, these algorithms are implemented in specialized languages for modern graphics cards (GPUs), due to their inherently parallel architecture. From this work stem two algorithm designs. Furthermore, four additional decoding algorithms and a clustering algorithms have been designed and implemented, which are also part of HLT1. Apart from that, an parallel Kalman filter algorithm has been designed and implemented, which can be used in both HLT stages. The developed algorithms satisfy the requirements of the LHCb collaboration for the LHCb upgrade. In order to execute the algorithms efficiently on GPUs, a software framework specialized for GPUs is developed, which allows executing GPU reconstruction sequences in parallel. Combining the developed algorithms with the framework, an execution sequence is completed as the foundations of a GPU HLT1. During the research carried out in this thesis, the aforementioned developments and a small group of collaborators coordinated by the author lead to the completion of a full GPU HLT1 sequence. The performance obtained on GPUs allows executing a reconstruction sequence in real-time, under LHCb upgrade conditions. The developed GPU HLT1 constitutes the first GPU high level trigger ever developed for an LHC experiment. Finally, various possible realizations of the GPU HLT1 to integrate in a production GPU-equipped data acquisition system are detailed