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Modeling TCP Throughput: an Elaborated Large-Deviations-Based Model and its Empirical Validation

Abstract : In today's Internet, a large part of the traffic is carried using the TCP transport protocol. Characterization of the variations of TCP traffic is thus a major challenge, both for resource provisioning and Quality of Service purposes. However, most existing models are limited to the prediction of the (almost-sure) mean TCP throughput and are unable to characterize deviations from this value. In this paper, we propose a method to describe the deviations of a long TCP flow's throughput from its almost-sure mean value. This method relies on an ergodic large-deviations result, which was recently proved to hold on almost every single realization for a large class of stochastic processes. Applying this result to a Markov chain modeling the congestion window's evolution of a long-lived TCP flow, we show that it is practically possible to quantify and to statistically bound the throughput's variations at different scales of interest for applications. Our Markov-chain model can take into account various network conditions and we demonstrate the accuracy of our method's prediction in different situations using simulations, experiments and real-world Internet traffic. In particular, in the classical case of Bernoulli losses, we demonstrate: i) the consistency of our method with the widely-used square-root formula predicting the almost-sure mean throughput, and ii) its ability to additionally predict finer properties reflecting the traffic variability at different scales.
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Contributor : Paulo Gonçalves Connect in order to contact the contributor
Submitted on : Tuesday, October 19, 2010 - 6:38:18 PM
Last modification on : Monday, January 24, 2022 - 11:50:02 AM


  • HAL Id : inria-00527636, version 1



Patrick Loiseau, Paulo Gonçalves, Julien Barral, Pascale Vicat-Blanc Primet. Modeling TCP Throughput: an Elaborated Large-Deviations-Based Model and its Empirical Validation. IFIP, Performance, Nov 2010, Namur, Belgium. ⟨inria-00527636⟩



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