Navegando por Assunto "Optical network"

Agora exibindo 1 - 4 de 4

Acesso aberto (Open Access)
Avaliação de desempenho de algoritmos de alocação de comprimento de onda em redes ópticas WDM
(Universidade Federal do Pará, 2010-03-29) BEZERRA, Paulo Henrique Gonçalves; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
In this work we studied Routing and Wavelength Assignment (RWA) algorithms on Wavelength Division Multiplexing Optical Networks. The objective to study the allocation algorithms first-fit, least-used and most-used is based on the strategy used to study the RWA Problem. The strategy builds on the overview of the problem involving the routing algorithms and algorithms for allocating wavelength, and having as a key metric for its blocking probability results. This paper presents a different perspective to the problem and believes that the allocation of wavelengths overlaps in importance to the action of routing in optical networks. This perception occurs when analyzing the problem RWA from the traditional criterion used in establishing a route: choosing the shortest path between origin and destination. Despite the identification of a shortest path is no guarantee in optical networks, it will be used, as is needed for that path, a wavelength appropriate. We used a simulation tool for WDM networks OWNS called to perform an analysis of the RWA problem. The results are presented graphically and in one of the simulations we observed a strong trend towards decrease in blocking probability and a good flow of traffic on the network thereby enabling an increase in transmission capacity of the network. Finally, this paper presents a discussion of the differences and limitations of this work and presents future research directions in this field of study.
Acesso aberto (Open Access)
Predição de falhas em redes de grades OBS com plano de controle GMPLS
(Universidade Federal do Pará, 2013-01-09) BECHARA, Mariana Castro; CERQUEIRA, Eduardo Coelho; http://lattes.cnpq.br/1028151705135221
This paper presents a proposal for predict failures in OBS grid network with GMPLS to assist applications in collaborative environments, like E-Science. Agents monitoring traffic (DQMA-Fuzzy) for related QoS parameters and others related to imperfections in optical links. A system based on fuzzy logic has been developed to give more robustness and flexibility in decision-making agents, because it presents a solution faster and easily implementable. NS-2 (Network Simulator – 2) simulations show that the proposed DQMA-Fuzzy is able to minimize blockages and balancing the use of grid resources, ensuring well-defined service levels, assisting in traffic engineering and fault prediction.
Acesso aberto (Open Access)
Tolerância a falha em redes ópticas de nova geração
(Universidade Federal do Pará, 2006-08-21) SOUZA, Jaime Viana de; ABELÉM, Antônio Jorge Gomes; http://lattes.cnpq.br/5376253015721742
Availability is of considerable concern for network desingners due to the increasing importance of the information that pass through them, motivating the providers of telecommunications services to improve the capacity and the qyality of their backbones. Optical networks that use WDM (“Wavelength Division Multiplexing”) usually present control plane based in GMPLS (Generalized Multiproptocol Label Switching) architecture, because it provides one batter adequation between IP protocol and the optical layer. This work makes a comparative study, between the machanisms used for the traditional optical networks and the said ones of the next generation. It considers the adoption of different mechanism of fault tolerance in function of the traffic model, through simulations, in the networks implemented by SDH/SONET architecture and simulations in computer of the main mechanisms offered for the multilayer model IP-GMPLS/WDM to provide protection and restoration of connectivity in nexte-generation optical networks, being objectified to authenticate our proposal.
Acesso aberto (Open Access)
Unsupervised learning algorithms for data-driven fault management in optical networks
(Universidade Federal do Pará, 2024-12-09) RIBEIRO, Andrei Nogueira; LOBATO, Fabrício Rossy de Lima; http://lattes.cnpq.br/6344884902408613; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
Over the past years, the emergence of more complex and bandwidth-hungry applications has charged efforts to ensure the reliability of optical networks. The occurrence of faults, for instance, can directly affect the quality of transmission of these optical systems, leading to several implications, including packet losses and service disruption. Hence, it is vital to mitigate faults in optical networks to guarantee the availability of the system and meet the service level agreement requirements. Moreover, as the complexity of optical networks evolves constantly, machine learning-based approaches have been proposed to deal with the system dynamics while providing automated fault management. In that regard, most proposed approaches are based on supervised learning (SL) models, which require large amounts of fault data to be properly trained. However, data from fault conditions are typically scarce in practical scenarios, which poses limitations for deploying SL-based models. Therefore, this work explores several unsupervised learning algorithms to perform fault management in optical networks. As fault data are absent in several real-world scenarios, unsupervised strategies trained with only data from normal operating conditions can be an effective alternative. These strategies disregard the need for data from abnormal network conditions and thus require much less data for model training. In this work, the fault detection and localization performances of cluster-based algorithms (K-means, Fuzzy C-means, Mahalanobis Squared-Distance-based model, and Gaussian Mixture Model) and dimensionality reduction-based approaches (Principal Component Analysis and Autoencoder) are compared leveraging a dataset derived from an optical testbed. The techniques are evaluated in terms of Type I (false-positive) and Type II (false-negative) error trade-offs. Ultimately, all techniques demonstrated satisfactory fault detection results when trained with only data from normal conditions, achieving an average accuracy of more than 90%. Such results suggest their applicability to real-world optical network fault management scenarios.