An important challenge in network virtualization is the process of assigning physical resources to virtual network requests, where virtual node resources represent the demands of IT capacities while virtual links are connected between pairs of virtual nodes. Due to their special characteristics, physical optical network infrastructures requires specific strategies to solve this problem, called virtual optical network embedding (VONE). This paper presents a survey on most relevant VONE schemes found in the literature. A discussion of important algorithmic aspects of this problem is presented, such as optical-layer restrictions, global performance metrics, and possible research directions. In addition, we propose a new taxonomy of VONE approaches, considering the type of optical networks, dynamic or static scenarios, and the optimization method.

This work introduces two novel approaches for the selection of active lightpaths that perform a spectrum defragmentation process in elastic optical networks (EONs). The algorithms, named DF-Ants and DF-Gen, are based on ant colony optimization and genetic metaheuristics, respectively, and their objective is to minimize the fragmentation of the entire network, evaluated with two different fragmentation metrics. In this way, the blocking probability is expected to be minimized with the fewest number of reconfigured possible connections. Furthermore, a new performance metric for spectrum defragmentation is also presented, named weighted blocking rate (WBR). Unicast traffic simulations were conducted, showing the feasibility of the proposal.

Wide-area optical networks face significant transmission challenges due to the relentless growth of bandwidth demands experienced nowadays. Network operators must consider the relationship between modulation format and maximum reach for each connection request due to the accumulation of physical layer impairments in optical fiber links, to guarantee a minimum quality of service (QoS) and quality of transmission (QoT) to all connection requests. In this work, we present a BER-adaptive solution to solve the routing, modulation format, and spectrum assignment (RMLSA) problem for wide-area elastic optical networks. Our main goal is to maximize successful connection requests in wide-area networks while choosing modulation formats with the highest efficiency possible. Consequently, our technique uses an adaptive bit-error-rate (BER) threshold to achieve communication with the best QoT in the most efficient manner, using the strictest BER value and the modulation format with the smallest bandwidth possible. Additionally, the proposed algorithm relies on 3R regeneration devices to enable long-distances communications if transparent communication cannot be achieved. We assessed our method through simulations for various network conditions, such as the number of regenerators per node, traffic load per user, and BER threshold values. In a scenario without regenerators, the BER-Adaptive algorithm performs similarly to the most relaxed fixed BER threshold studied in blocking probability. However, it ensures a higher QoT to most of the connection requests. The proposed algorithm thrives with the use of regenerators, showing the best performance among the studied solutions, enabling long-distance communications with a high QoT and low blocking probability.

In real-world multi-objective optimization, dealing with many objectives and a large number of solutions is a common challenge that complicates data visualization and analysis. This study aims to simplify decision-making by analyzing tools to better explore Pareto optimal solutions in many-objective scenarios, integrating clustering, filtering, and ranking with existing graphics techniques. The dynamic combination of these tools should reduce complexity and highlight significant patterns in the data set, allowing decision-makers to tailor the visualization to their specific needs and preferences. Central to the approach presented in this work is the innovative application of shape-based clustering to organize the solution set and the use of this clustering to define distinct types of filters. Additionally, ranking methods originally proposed to enhance search in many-objective evolutionary algorithms are used here to identify the best solutions based on predefined criteria in combination with other techniques. The efficacy of the proposed integrated approach was evaluated using an application developed with this aim and considering a five-objective problem as a case study. The analysis suggests that using these combined strategies aids interactive visual exploration, effectively reducing solution volume and improving data understanding, potentially facilitating decision-making tasks.

Substitution boxes, or S-boxes, are critical elements of symmetric block cipher algorithms—these being the ones that provide “confusion” (a concept defined by Claude Shannon). This work presents a method for generating $8\times 8$ S-boxes using a multi-objective evolutionary algorithm, aiming to simultaneously optimize Nonlinearity (NL) and the Strict Avalanche Criterion (SAC), which are two important cryptographic properties of S-boxes. Chaotic maps were used to generate the initial population. Experimental executions were carried out, and the results were compared to evaluate the different Pareto fronts obtained. The results indicate that the proposed versions of our algorithm achieve good performance, comparable to the state of the art (with only 150 generations of execution), and it was also found that they continue to improve for runs of 5000 generations. This method can be used for the generation of S-boxes with specific properties.