doi:10.1016/j.peva.2007.05.003
Copyright © 2007 Elsevier Ltd All rights reserved.
An efficient technique to analyze the impact of bursty TCP traffic in wide-area networks
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Michele Garettoa,
,
and Don Towsleyb, 
aComputer Science Department, University of Torino, Italy
bComputer Science Department, University of Massachusetts, Amherst, MA, United States
Received 22 December 2005;
revised 1 September 2006;
accepted 25 May 2007.
Available online 2 June 2007.
Abstract
In this paper we describe an analytical technique for the performance evaluation of wide-area networks carrying realistic TCP traffic, such as that produced by a large number of finite-sized connections transferring files whose sizes are taken from a long-tail distribution. The analytical predictions are validated against detailed simulation experiments, and prove to be accurate and robust under a variety of operating conditions. The model also provides original insights into the impact on the network of long-tail flow length distributions, and allows the effectiveness of “TCP pacing” in reducing the traffic burstiness to be evaluated analytically. Our contribution is a performance evaluation methodology that could be usefully employed in network dimensioning and engineering.
Keywords: TCP; Traffic burstiness; Network performance; Markovian models; Queueing analysis
Fig. 1. Simple dumb-bell topology for open loop analysis.
Fig. 2. Queue length distributions obtained from simulation and different analytical models when g=0.8.
Fig. 3. Stochastic finite state machine of TCP.
Fig. 4. Examples of window size evolution without loss (left diagram) or with a loss at position 6 (right diagram), for a connection entering the slow start phase in state E10, with 12 packets remaining.
Fig. 5. Comparison of batch size distributions according to simulation and model for the scenario in Fig. 1.
Fig. 6. Effect of pacing on queue length distribution at load g=0.9. Comparison between simulation (left) and model (right).
Fig. 7. Fitting of Pareto distribution with 10 exponentials.
Fig. 8. Queue length distributions obtained using the fitted Pareto distribution of flow length, for different link capacities.
Fig. 9. Distributions of the number of active flows measured on simulation for different values of link capacity.
Fig. 10. Example of convergence of the FPA in the absence of unnecessary retransmissions.
Fig. 11. Example of convergence of the FPA in the presence of unnecessary retransmissions.
Fig. 12. Simulation trace of the number of active connections, g=0.986,
.
Fig. 13. Packet loss probability in the single bottleneck scenario, according to simulation and analysis, varying the system load g.
Fig. 14. Average queue length in the single bottleneck scenario, according to simulation and analysis, varying the system load g.
Fig. 15. Meshed network topology (left) and associated traffic matrix (right).
Fig. 16. Comparison of average packet loss probabilities obtained from simulation and analysis for the queues of the meshed topology.
Fig. 17. Comparison of average buffer occupancy obtained from simulation and analysis for the queues of the meshed topology.
Fig. A.1. A portion of the Markov chain of the
queue model showing all transitions entering and leaving the generic state (i,n).
Table 1.
Transitions of the SFSM

* denotes sequences of packets to be further split into batches.
Table 2.
Conditions on the indexes of the transitions in Table 1

Table 3.
Comparison of average and standard deviation of queue lengths (in packets)

An extended version of the paper “Modeling, Simulation and Measurements of Queuing Delay under Long-tail Internet Traffic”, that appeared at ACM SIGMETRICS ’03, San Diego, CA, June 2003.

Corresponding address: Dipartimento di Informatica, Universita’ di Torino, Corso Svizzera 185, 10149 Torino, Italy. Tel.: +39 0116706844; fax: +39 011751603.