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Predicting the throughput of large TCP transfers is important for a
broad class of applications. This paper focuses on the design,
empirical evaluation and analysis of TCP throughput predictors. We
first classify TCP throughput prediction techniques into two
categories: Formula-Based and History-Based. Within each class, we
develop representative prediction algorithms, which we then
evaluate empirically over the RON testbed. FB prediction relies on
mathematical models that express the TCP throughput as a function
of the characteristics of the underlying network path. It does not
rely on previous TCP transfers in the given path and it can be
performed with non intrusive network measurements. We show that the
FB method is accurate only if the TCP transfer is window-limited to
the point that it does not saturate the underlying path, and
explain the main causes of the prediction errors. HB techniques
predict the throughput of TCP flows from a time series of previous
TCP throughput measurements on the same path, when such a history
is available. We show that even simple HB predictors, like Moving
Average and Holt-Winters, using a history of few and sporadic
samples can be quite accurate
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