Title:
On the Predictability of Large Transfer TcP Throughput
On the Predictability of Large Transfer TcP Throughput
Author(s)
Dovrolis, Constantine
Ammar, Mostafa H.
He, Qi
Ammar, Mostafa H.
He, Qi
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Abstract
With the advent of overlay and peer-to-peer networks,
Grid computing, and CDNs, network performance prediction
becomes an essential task. Predicting the throughput of large
TCP transfers, in particular, has attracted much attention. In this
work, we focus on the design, empirical evaluation, and analysis
of TCP throughput predictors for a broad class of applications.
We first classify TCP throughput prediction techniques into two
categories: Formula-Based (FB) and History-Based (HB). 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 network
path (e.g., RTT, loss rate, available bandwidth). FB prediction
does not rely on previous TCP transfers in the given path, and it
can be performed with non-intrusive network measurements. We
show, however, 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, such as Moving Average and Holt-Winters,
using a history of limited and sporadic samples, can be quite
accurate. On the negative side, HB predictors are highly pathdependent.
Using simple queueing models, we explain the cause
of such path dependencies based on two key factors: the load on
the path, and the degree of statistical multiplexing.
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Date Issued
2005
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535555 bytes
Resource Type
Text
Resource Subtype
Technical Report