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Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time. [ABSTRACT FROM AUTHOR]

Traffic demand in access networks has grown in recent years, and service providers need to upgrade their infrastructure to the latest access standards. While fiber has become the preferred technology of choice in access networks, there are many fibre access technologies available in the market. This poses challenging questions to operators that are not always easy to answer: How to upgrade? What technology and for how long wil it cope with the demands? In this article we model the traffic forecast in access networks for the next decade and analyze possible upgrade scenarios of fibre access networks, concluding which of the NG-PON flavors could better fit the demand. [ABSTRACT FROM AUTHOR]

States that the large number of processing elements in current parallel systems necessitates the development of more comprehensive and realistic tools for the scalability analysis of algorithms on those architectures. Presentation of a simple analytical tool with which to study the scalability of parallel algorithm-architecture combinations.