We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.Index Terms-Case-based reasoning (CBR), cognitive networks, impairment-aware networking, quality of transmission (QoT), wavelength-routed optical network (WRON).

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Greening the Internet" is an important research topic in the last years. The Internet capacity and energy consumption have increased, and the utilization of design and operation techniques to reduce this consumption are a must. In this paper, we present a multiobjective genetic algorithm to design virtual topologies for reconfigurable wavelength-routed optical networks with the aim of reducing both the energy consumption and the network congestion while ensuring that the lightpaths of the virtual topologies fulfill quality of transmission requirements. Moreover, we also present another version of that method enhanced with cognitive techniques, and we show, by means of simulation, the performance advantages brought when introducing these cognitive techniques.

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