Abstract

Recently statistical examination of World-Wide Web (Web) traces have shown evidence that Web traffic arising from the file transfers exhibits a behavior that is consistent with the notion of self-similarity. Essentially, self-similarity indicates that significant burstiness is present on a wide range of time scales. We conjecture that Web traffic exhibits characteristics spanning different time scales and investigate how such traffic can be modeled by non-parametric methods. For this purpose, we present a forecasting strategy based on the wavelet decomposition of the original time series into varying scales of temporal resolution, and apply it to Web traffic data. The wavelet transform provides a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. An extensive set of HTTP logs is converted to a univariate traffic time series on the basis of the average number of bytes transferred over a one-minute period. We f...

Citations