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This paper is focused on modeling Web request and session level arrival processes. We propose a statistically rigorous approach which includes testing for non-stationarity and Gaussianity, and uses model selection criterion. Furthermore, a goodness of fit test is applied to each candidate model - ARMA, ARIMA, FARIMA, and FGN - and for validation purpose real data is compared with data simulated from the models. The results based on data extracted from six Web servers with different workload intensities show that (1) there is no one-fits-all solution and (2) servers with high workloads have both request and session traffic modeled well with FARIMA model which is capable of capturing both long-range and short-range dependence.