Quality of Service in Multiservice IP Networks: Third by Giovanni Neglia, Vincenzo Falletta, Giuseppe Bianchi

By Giovanni Neglia, Vincenzo Falletta, Giuseppe Bianchi (auth.), Marco Ajmone Marsan, Giuseppe Bianchi, Marco Listanti, Michela Meo (eds.)

This e-book constitutes the refereed complaints of the 3rd foreign Workshop on caliber of provider in Multiservice IP Networks, QoS-IP 2005, held in Catania, Italy in February 2005.

The 50 revised complete papers awarded have been rigorously reviewed and chosen from round a hundred submissions. The papers are prepared in topical sections on analytical versions, 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 certain environments, and scheduling.

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Network calculus has successfully been applied to derive service guarantees for per-flow Integrated Services networks. Recent extensions also allow providing performance bounds for aggregate-based Differentiated Services domains, but with a significant increase in complexity. Further on, a number of issues still remain unsolved or are not well understood yet. Founded on convolution and de-convolution, network calculus obeys a strong analogy to system theory. However, system theory has been extended beyond the time domain, applying the Fourier transform and allowing for an efficient analysis in the frequency domain.

The Win-B model moderately overestimates the average queue length and loss probability, as pointed out in [19]. However, for very short flows, completion time predictions obtained with the Win-B model are slightly optimistic; this is mainly due to the fact that an idealized TCP behavior (in particular, without timeouts) is considered in the model. 2 Impact of the Flow Size We now discuss the ability of our model to capture the impact on the network behavior of the flow size variance. 3”). 32 (this is the average flow size used in the previous subsection), and we vary the standard deviation σ.

Class 1 class 2 Q1 Q2 class 3 load of class 2 Mb/s 65 50 65 80 95 Fig. 4. Network topology of the flash crowd scenario. 145 time (s) Fig. 5. Temporal evolution of the load produced by the flash crowd. Of Mice and Models 29 Table 5. 2, having introduced random elements. ALP Distr. 0 Distr. 0 Distr. 01 10−5 Distr. 00 10−6 Distr. 85 For the sake of comparison, in this case we also show in Figure 10 the predictions generated by the fully deterministic fluid model. The fact that in this case the network becomes overloaded allows the deterministic fluid model to correctly predict congestion.

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