Bacharelado em Ciência da Computação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/6
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Resultados da Pesquisa
Item Computação conjunta e alocação de recurso para offloading em computação de borda: um mapeamento sistemático(2023-09-15) Lócio, Daniel Mariz; Domingues, Jeísa Pereira de Oliveira; http://lattes.cnpq.br/1291084760973085; http://lattes.cnpq.br/3920880960316221This work presents a systematic mapping of articles published between the years 2016 and 2023 on Offloading in edge computing, or Multi-access Edge Computing (MEC), considering the aspects of joint computation and resource allocation. MEC is a technology that aims to reduce latency and increase efficiency by processing data close to its source, a necessary approach for the future of computer networks. Based on the proposed mapping, this present work discusses various techniques, methods, and models from the reviewed articles. In this work, the main challenges faced in this area are studied, as well as the approaches proposed by the analyzed articles. The result of this mapping is a classification of the field, providing a comprehensive and detailed overview of recent advancements in edge computing, with special attention given to joint computation and resource allocation solutions that enhance the performance and efficiency of the offloading technique.Item Uso de técnicas de detecção de comunidades para análise de redes ópticas(2021-12-09) Barros, Jonas Freire de Alcântara Marques de; Araújo, Danilo Ricardo Barbosa de; http://lattes.cnpq.br/2708354422178489; http://lattes.cnpq.br/6917406943428049The growth in the use of services on the Internet has promoted a increasing demand for high transmission rates. This demand have been met by optical networks. At the design stage of these networks, the engineer must be able to assess the performance of a given network before its actual physical implementation. In this design process, several topologies are considered. The comparison between topologies is made through metrics that indicate a certain aspect of the network. Typically the metrics considered are performance indicators, such as Throughput, Blocking Probability, Resilience and also other indicators, such as the network Cost. Performance indicators are important because they inform about the quality of a particular topology. Therefore, performance metrics are essential for the design projects of such networks. The most reliable way to calculate the values of these performance indicators is through simulations. However, simulations have a high computational cost, increasing the time needed to obtain information about topologies; since, in these projects, a very large number of different topologies must be considered. On the other hand, a large number of researches in the most diverse domains of knowledge have been carried out on the theme of community detection in graphs. However, there are no applications of these techniques in high capacity fiber-optic networks. Thus, the present work aims to investigate the existence of a correlation between the ability of a fiber-optic network to form communities and its performance indicators. More specifically, it’s Blocking Probability and indicators of Resilience. The analysis was performed comparing the Blocking Probability and Resilience of these networks and the clustering metrics using scatter plots. According to the results, there is a positive correlation between the community metrics and the network performance indicators, and comparatively a speedup of approximately 4,500 times was obtained between the community metrics and the simulations.Item Projeto integrado de redes ópticas de longa distância e Metropolitanas usando algoritmos de inteligência computacional: estudo de caso para o estado de Pernambuco(2017) Nascimento, Jorge Candeias do; Araújo, Danilo Ricardo Barbosa de; http://lattes.cnpq.br/2708354422178489; http://lattes.cnpq.br/8065833426856653Nowadays, several network technologies with different prices and adaptations are appearing in the market. A network topology project involves several metrics; the metrics are used to evaluate a project. In the evaluation we use metrics such as robustness metrics (which help in the network’s ability to recover from a failure), blocking probability and energy consumption. The best way to optimize infrastructure in a network design would be to use the latest technologies, only the most efficient ones, even if such technologies are more expensive. However, of the metrics to be considered in this type of project, one of them is the cost (capital employed). Therefore, it is not always feasible to use the most expensive ones on the market. Many technical issues can help control the metrics of these projects, among which is the network topology (link interconnection). Multiobjective evolutionary algorithms (algorithms inspired by the evolution of the species) have been studied in the state of the art for the conception of network topologies. At the same time, clustering algorithms (algorithms specialized in separating samples into groups) have been used in other types of network studies. This study aimed to make use of computational intelligence algorithms in the construction of a network topology project, using the state of Pernambuco as a case study. In a first stage of the study, a clustering algorithm was used in the division of the state into groups. The intention of this part of the work was to measure the coverage of the network in relation to the entire size of the state, and thus ensure the completeness of the network. In addition, the clustering stage also aimed to propose a cost control model through the merging of different technologies for the network (Passive or active) depending on the function of the network segment. In a second step, an evolutionary multiobjective algorithm was used to compose several network topologies that served the clusters created in the previous step. This algorithm has evolved the various network topologies in order to improve four metrics, Blocking Probability, Cost, Energy Consumption and Algebraic Connectivity. The multiobjective algorithm was designed as a memetic algorithm, and, after a set of executions, the algorithm performances were compared with and without the alteration. The results of the tests, in the first stage, showed that the clustering techniques are quite efficient and adaptable to the proposed goal both in terms of network completeness and cost control. Already in the second stage, or multiobjective search stage, it was verified, through the use of a quality indicator (hypervolume), that there was an improvement of the algorithm in relation to convergence and diversity to the Pareto curve, with the use in its new form as memetic algorithm.Item Proposta de um meta-modelo para avaliação de robutez de redes de computadores com base na combinação de métricas topológicas(2017) Barros, Gustavo Henrique Pinto Soares de; Araújo, Danilo Ricardo Barbosa de; http://lattes.cnpq.br/2708354422178489; http://lattes.cnpq.br/1155438495823549A growing demand for resilience and robustness in the field of computer networks rises from the great diversity of its aplications. The modern sistems display an increasing critical nature, and the occurrence of perturbations may cause significant losses either human, monetary or environmental. Optical fiber acts on the current systems as the main mean of transportation. Among its variety of applications, which are heavily dependant on its infrastructure, some of them are the internet, cable television and high transmission rates systems. The non-homogeneous and complex topology nature of these networks determine their increasing avaluation cost. For these reasons, optical networks are the study object of this research. Quantifying the robustness of networks is usually accomplished by nodes and links failure simulations, on which the monetary and temporal cost scales proportionally to the network size. This research analyzes the possibility of obtaining values of robustness metrics in complex networks which would originally be obtained from simulations through an alternative regression method. This method has as inputs the values of simple metrics which are obtained through applications other than simulations and uses artificial neural networks to forecast simulation results in a smaller period. The results are obtained through a comparison between the proposed model output and the node and link failure simulation output. They indicate that the proposed model presents a satisfactory error margin, between 10−³ and 10−9, thus the simulation values were reached successfully through regression on a smaller time period.