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The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models' performance. [ABSTRACT FROM AUTHOR]

The increasing trend of global IP traffic is mainly driven by high-definition video services and cloud computing and storage. Moreover, to maintain a high quality of service in content delivery networking, data are geographically replicated in data centers distributed within network topologies. Thus, data centers are an emerging scenario for research and development aimed at energy-efficient transmission and networking solutions. Previous research work has focused on intradata center energy efficiency while interdata center energy issues have not been extensively analyzed yet. We propose heuristics and meta-heuristics for optimal placement of data centers with minimum power consumption over a network topology relying on flex-grid spectral use. In order to minimize the network's power consumption, we have performed a detailed comparison of heuristic and meta-heuristic designs for different network scenarios based on real topologies. Moreover, our results show that meta-heuristic provides an optimum data center placement in a reasonable amount of time for a small- to medium-sized network. [ABSTRACT FROM AUTHOR]