Daniel A. R. Chaves

OSNR model to consider physical layer impairments in transparent optical networks

We propose a model that considers several physical impairments in all-optical networks based on optical signal-to-noise degradation. Our model considers the gain saturation effect and amplified spontaneous emission depletion in optical amplifiers, coherent crosstalk in optical switches, and four-wave mixing in transmission fibers. We apply our model to investigate the impact of different physical impairments on the performance of all-optical networks. The simulation results show the impact of each impairment on network performance in terms of blocking probability as a function of device parameters. We also apply the model as a metric for impairment-constraint routing in all-optical networks. We show that our proposed routing and wavelength assignment algorithm outperforms two common approaches.

Wavelength Assignment for Physical-Layer-Impaired Optical Networks Using Evolutionary Computation

This paper presents a wavelength assignment algorithm suitable for optical networks mainly impaired by physical layer effects, named the Intelligent Wavelength Assignment algorithm (iWA). The main idea is to determine the wavelength activation order for a first-fit algorithm that balances the impact of the physical layer effects by using a training algorithm inspired by evolutionary concepts. The iWA presents some recently proposed concepts in intelligent optimization algorithms, such as an external archive to store the best solutions. Some different physical layer effects, such as four-wave mixing and residual dispersion, were considered in the tests of our proposal. We tested our proposal for transparent optical networks. However, we believe iWA can be used in other types of network, such as opaque networks and translucent networks. The proposed wavelength assignment algorithm was compared with five other wavelength assignment algorithms for two network topologies in three different scenarios. The iWA algorithm outperformed the other WA algorithms in most cases. The robustness of our proposed algorithm to the load distribution changes was also analyzed.

A performance comparison of multi-objective optimization evolutionary algorithms for all-optical networks design

In this paper we investigate the performance of well known multi-objective optimization evolutionary algorithms (MOEA) applied to the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to both minimize the total cost to build the network, i.e. the capital expenditure, and to maximize the overall network performance. We used the network blocking probability to assess the quality of the network service. We have considered the following five different MOEA: NSGAII, SPEA2, PESAII, PAES and MODE. In order to suggest a suitable algorithm to solve the problem, we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions. We used four well known metrics to compare the achieved Pareto Fronts: hypervolume, spacing, maximum spread and coverage. From our results, we believe that the NSGAII and the SPEA2 algorithms are more suitable to solve this specific problem.

Fast and adaptive impairment aware routing and wavelength assignment algorithm optimized by offline simulations

In all-optical networks, signals are transmitted through optical physical layer with no regeneration. Therefore, noise accumulation along lightpath can severely impair optical signal-to-noise ratio. Impairment aware routing and wavelength assignment algorithms (IA-RWA) can take into account these effects, improving the network performance. In this paper we describe a fast and high performance adaptive weight function to be used as the metric for the routing algorithm in optical networks constrained by physical impairments. The input information for this function are link availability, route length and two adjustable parameters. These two parameters provide information about the network impairments. An offline simulation must be run to adjust them prior to the online network operation. The main advantage of this approach is the use of simple network parameters during the routing process instead of a complex optical noise based formulation, which renders it a better performance in terms of both, time to find a route and blocking probability. In our simulations we considered three physical layer effects: ASE noise generation, Optical Amplifier gain and ASE saturation and OXC crosstalk. We performed a performance comparison between this new metric and other metrics previously described in the literature.

A multi-objective approach to design all-optical and translucent optical networks considering CapEx and QoT

In this paper we propose a methodology based on an evolutionary multi-objective optimization algorithm, called NSGA-II to design the topology and define the devices for both all-optical and translucent optical network. We aim to define the topology layout and the specification of the optical devices that should be deployed in the network in order to minimize simultaneously the total installation cost of a communication network (CapEx) and the total network blocking probability (performance criterion). To accomplish that, we propose a capital cost model for the network. We considered the following physical layer impairments: losses in optical devices, amplified spontaneous emission in optical amplifier and homodyne crosstalk in optical cross connect, polarization mode dispersion and residual dispersion. Our proposed methodology can solve the network topology design problem taking into account the physical layer impairments and the capital costs simultaneously. We also present a case study to show the effectiveness of our methodology to define the degree of transparency of the network.

A methodology to design the link cost functions for impairment aware routing algorithms in optical networks

We propose a methodology to design the link cost function and, consequently, a systematic form to design a RWA algorithm. We call this methodology link cost function design (LCFD) and it consists of four steps: The choice of the link cost function input variables, the expansion of the cost function in terms of a series, the selection of an overall network performance indicator as the optimization target, and finally, the execution of an optimization process to find the series coefficients that optimize the network performance indicator based on off-line network simulations. The optimization process is performed by a computational intelligence technique, the particle swarm optimization. The proposed methodology (LCFD) is used to design an adaptive IA-RWA algorithm, which we call Power Series Routing (PSR). The effectiveness of both methodology and IA-RWA algorithm is investigated. The PSR is compared with other algorithms found in the literature by means of computational simulations and our proposal presented lower blocking probabilities with shorter computation time. Furthermore, we investigate the sensitivity and the ability of the proposed PSR to adapt itself to topological changes in the network due to both link/node addition/failure. We also investigate the behavior of the PSR in a scenario where the traffic load distribution is randomly chosen (non-uniform traffic), and we compared it to other three routing algorithms.

Intelligent and fast IRWA algorithm based on power series and Particle Swarm Optimization

In all-optical networks signals are transmitted through physical layer with no regeneration. Therefore, physical impairments along lightpath can severely reduce network performance. For this reason, many efforts have been made to develop impairment aware routing and wavelength assignment algorithms (IRWA) in order to mitigate the impairments effects, improving the network performance. In this paper we propose and analyze the performance of an adaptive impairment aware routing algorithm based on a set of chosen input network parameters. The cost function of this routing algorithm is based on a power series expansion. The routing algorithm, called Power Series Routing (PSR), is trained by an optimization technique called Particle Swarm Optimization. We show that this IRWA algorithm can learn during the training stage and adapt itself to the network conditions.

An evolutionary spectrum assignment algorithm for Elastic Optical Networks

A fundamental issue in Elastic Optical Networks (EONs) relies on choosing a proper route and necessary number of contiguous frequency slots from end-to-end to accommodate the traffic demands. Spectrum assignment based on the traditional First-Fit assignment has been extensively employed in EON investigations due to its inherent simplicity and favorable capacity of leaving end-to-end free slots in the network. This paper proposes a possible structure of an evolutionary algorithm that can be used to search for an appropriate spectrum ordering of the First-Fit spectrum assignment to mitigate request blocking probability. We here analyse the path request blocking probability when just lack of resource is emphasized. However, the proposal is general enough to be used in other situations.

Design of transparent optical networks considering physical impairments, CAPEX and energy consumption

We propose a methodology which applies a multi-objective Evolutionary Algorithm, the NSGAII, in order to design transparent optical networks, aiming to minimize simultaneously the total cost to build the network, the blocking probability and the energy consumption during operation. The optimizer provides a set of non-dominated solutions and, after that, the designer can decide which solution is more suitable for a specific case. We believe these three different important aspects must be taken into account during the design process. However, just some few papers tackle energy consumption from a physical network design perspective. Besides, to the best of our knowledge, none of the previous presented proposals consider all these issues simultaneously.

An efficient multi-objective evolutionary optimizer to design all-optical networks considering physical impairments and CAPEX

In this paper we propose efficient operators for a well known multi-objective evolutionary optimizer, called NSGA II, applied to design all-optical networks regarding the network topology and the device specifications in order to both minimize the capital expenditure to build the network and to maximize the overall network performance. From the experiments, we perceived that it is better to use an uniform crossover, to include preferences a priori and to initialize the individuals emphasizing the network topology.