On the Benefits of Traffic Segregation in WDM Networks

Mining association rules for HIV-1 protease cleavage site prediction

Fernando TrazziMoises RibeiroHelio Waldman

This paper investigates whether performance of WDM optical switching may be improved by traffic shaping. Heavy-tailed Pareto traffic transmission split into different wavelengths, according to the holding time, is here proposed as a means of traffic shaping aimed at the minimization of mean delay, jitter, or blocking-time probability in optical burst switching. It is found that burst segregation across n wavelengths (n x M/PT/1) may outperform classical WDM (M/P/n) systems as far as overall residual-service time is concerned. The existence of optimal segregation thresholds is analytically demonstrated. Results for the whole traffic show one order of magnitude reduction in mean delay, while jitter and blocking-time are, respectively, two and three orders of magnitude better than the classic WDM non-segregated approach. Sandro da Silva CamargoPaulo Martins Engel

Several machine learning techniques, like neural networks, nonlinear support vector machines and decision trees, have been used to model the specificity of HIV-1 protease and to extract specific patterns from peptides cleaved by this protease. Despite many studies, no perfect rules are already known to determine the cleavage of a peptide by HIV-1 protease. These rules are useful for designing specific and efficient HIV inhibitors. Our results show that the technique of mining association rules can find several specificity rules of HIV-1 protease which presents 100% of cleavage probability. Recent papers on this subject show results in which the best rules present cleavage probability ranging from 16% to 91%.

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