This work proposes a method for the evaluation of the effectiveness of adopting dynamic spectral management (DSM) algorithms in different DSL scenarios. In the last years several DSM algorithms emerged in the literature but their comparison has been typically conducted within few scenarios and considering specific operating points. This work proposes the adoption of the DSM effectiveness factor (DEF) as a figure of merit capable of comparing the volumes of the whole rate regions, which expresses the set of operating points in the Pareto front. A random scenario generator was used to obtain four hundred DSL scenarios and compared flat power backoff (PBO) and DSM algorithms of levels 1 and 2. Besides confirming well-known facts, such that the effectiveness of DSM is significant in near-far scenarios, the results based on the proposed DEF allow to quantify the gains in bit rate that DSM can bring.

In this work, we propose an adaptive predictive flow control scheme based on the TSK fuzzy model for congestion control in broadband networks. The proposed control scheme intends to avoid congestion by applying a TSK type model to predict the buffer queue length. For this end, we developed an adaptive training algorithm for the TSK model that was incorporated into the proposed control scheme, achieving low loss rate network performance. Our developed fuzzy predictor consists of a two-step algorithm: an adaptive training stage with covariance resetting and a gradiente-based learning algorithm for refining the previous part of the prediction procedure. An evaluation of the proposed predictor is carried out by using real network traffic traces. As essential part of the proposed congestion control scheme, we derive an analytical expression involving the fuzzy model parameters for the control of the flow rates that minimizes the queueing length variance. Further, a network environment is considered and the congestion control is applied to each node of the analyzed

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