Probability
SIGCOMM '95 Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
The performance of TCP/IP for networks with high bandwidth-delay products and random loss
IEEE/ACM Transactions on Networking (TON)
The macroscopic behavior of the TCP congestion avoidance algorithm
ACM SIGCOMM Computer Communication Review
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Modeling TCP throughput: a simple model and its empirical validation
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Comparative performance analysis of versions of TCP in a local network with a lossy link
IEEE/ACM Transactions on Networking (TON)
Analytic models for the latency and steady-state throughput of TCP tahoe, Reno, and SACK
IEEE/ACM Transactions on Networking (TON)
STOCHASTIC DIFFERENTIAL EQUATION FOR TCP WINDOW SIZE: ANALYSIS AND EXPERIMENTAL VALIDATION
Probability in the Engineering and Informational Sciences
A Markov model of TCP throughput, goodput and slow start
Performance Evaluation - Special issue: Distributed systems performance
Performance Evaluation - Special issue: Distributed systems performance
A stochastic model of TCP/IP with stationary random losses
IEEE/ACM Transactions on Networking (TON)
A stochastic model for the throughput of non-persistent TCP flows
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Equilibria of a class of transport equations arising in congestion control
Queueing Systems: Theory and Applications
Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed
International Journal of High Performance Computing Applications
Compound TCP with Random Losses
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Investigating self-similarity and heavy-tailed distributions on a large-scale experimental facility
IEEE/ACM Transactions on Networking (TON)
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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 an important issue, 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's variability at different scales.