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Optical networks have been considered as the most capable technology for supplying the ever-increasing network bandwidth demand generated by the new Internet services. However, a significant challenge for optical networks is to provide an efficient manner to recover the lost communications due to failures. A failure in a system component or an optical fiber link can shut down all the crossing lightpaths, which may lead to a decrease in the revenue for clients or some sanctions to the providers due to an unattended agreed service level. In this work, we propose an adaptive–alternative path restoration algorithm, named NrPSR-R. The proposal has an adaptation capability according to the network state. NrPSR-R finds the Nr routes with minimum cost and selects one of them using a pre-defined policy. We performed a parametric analysis of the NrPSR-R algorithm. We also compared our proposal to other well-known restoration algorithms on different scenarios and NrPSR-R outperformed the others. [ABSTRACT FROM AUTHOR]

The design of optical networks is frequently accomplished by using evolutionary algorithms (EAs). However, the overall optimization process presents a huge execution time. In this letter, we propose a new method based on factorial design and geographical graph models to design optical networks. We propose an iterative graph generator based on Gabriel graphs that considers the number of deployed fibers and traffic demand. We focus on the specification of the fiber topology and optical devices aiming at finding a good trade-off in terms of capital expenditure and blocking probability. Our proposal provides high quality solutions with a very small execution time when compared to EAs. From our results, our proposal spends less than 1% of the time required by EAs and achieves better results. [ABSTRACT FROM PUBLISHER]

In this letter we extend the analysis of the Power Series Routing (PSR) algorithm, which was previously proposed and analyzed for transparent networks only, to the translucent and opaque networks. The PSR algorithm is a bio-inspired impairment-aware routing algorithm that can be optimized and can adapt itself to the network conditions. We demonstrate that the PSR algorithm is also suitable for these network configurations. We compared the PSR algorithm with other well known routing algorithms and we observed that the PSR algorithm achieved the best performance in all cases. [ABSTRACT FROM PUBLISHER]

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. [ABSTRACT FROM AUTHOR]