By Giovanni Neglia, Vincenzo Falletta, Giuseppe Bianchi (auth.), Marco Ajmone Marsan, Giuseppe Bianchi, Marco Listanti, Michela Meo (eds.)
This publication constitutes the refereed lawsuits of the 3rd foreign Workshop on caliber of carrier in Multiservice IP Networks, QoS-IP 2005, held in Catania, Italy in February 2005.
The 50 revised complete papers offered have been rigorously reviewed and chosen from round a hundred submissions. The papers are geared up in topical sections on analytical types, site visitors characterization, MPLS failure and recovery, community making plans and dimensioning, DiffServ and InfServ, routing, software program routers, community architectures for QoS provisioning, multiservice in instant networks, TCP in specified environments, and scheduling.
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Extra resources for Quality of Service in Multiservice IP Networks: Third International Workshop, QoS-IP 2005, Catania, Italy, February 2-4, 2004, Catania, Italy, February 2-4, 2004. Proceedings
The existence of two equilibrium points at loads close to 1, one stable and the other unstable, was also described in . This pseudo-chaotic dynamics of deterministic fluid models reflects a behavior that can be observed in ns-2 simulations. See for example the ns-2 sample path reported in Figure 11, for the same scenario. In this case congestion at the first queue persists for a very long time, until random fluctuations bring down the system to the stable operating point. 5 Conclusions In this paper we have defined a class of fluid models that allows reliable performance predictions to be computed for large IP networks loaded by TCP mice and elephants, and we have proved the accuracy and the flexibility of such models under static and dynamic traffic scenarios comprising just mice.
Correspondingly, 9 classes of TCP mice are considered in our model. The maximum window size is set to 64 packets for all TCP sources. 9. Fig. 1 compares the queue lengths distributions obtained with ns-2, and with the stochastic fluid model. 1), different approaches have been tried to model the traffic emitted by sources: – Poisson: the emitted traffic is a Poisson process with time-varying rate; – Det-B: the emitted traffic is a batch Poisson process with time-varying rate and constant batch size, equal to the instantaneous average TCP mice window size; – Exp-B: the emitted traffic is a batch Poisson process with time-varying rate and exponential batch size, whose mean is equal to the instantaneous average TCP mice window size; – Win-B: the emitted traffic is a batch Poisson process with time-varying rate, in which the batch size distribution is equal to the instantaneous TCP mice window size distribution.
0001 1e-05 1e-06 0 200 400 600 800 Number of packets in the queue 1000 Fig. 2. Queue size distribution for single drop tail bottleneck, varying the flow length distribution. 0 200 400 600 800 Number of packets in the queue 1000 Fig. 3. Queue size distribution for single drop tail bottleneck, varying the bottleneck capacity. time-varying load that follows the profile shown in Figure 5, and determine a temporary overload of the second queue between t1 = 50 s and t2 = 95 s. Figures 6 and 8 report, respectively, the queue lengths and the source window sizes versus time, averaging values obtained during intervals of duration 1 s for a better representation.