We consider the problem of estimating blocking probabilities in a multicast loss system via simulation, applying the static Monte Carlo method with importance sampling. An approach is introduced where the original estimation problem is first decomposed into independent simpler sub-problems, each roughly corresponding to estimating the blocking probability contribution from a single link. Then we apply importance sampling to solve each sub-problem. The importance sampling distribution is the original distribution conditioned on that the state is in the blocking state region of a single link. Samples can be generated from this distribution using the so called inverse convolution method. Finally, a dynamic control algorithm is used for optimally allocating the samples between different sub-problems. The numerical results demonstrate that the variance reduction obtained with the method is remarkable, between 400 and 36000 in the considered examples.

To realize a packet count classifier, in addition to the active flow table, a candidate table is needed, where information on flow candidates is kept. We observe the temporal behavior of both the active flow table and flow candidate table size using actual traffic traces. The results indicate that the performance bottleneck in a packet count classifier lies within the candidate table management. Also, the changes in the candidate table size occur much faster than in the active flow table. Therefore, fast methods of creating entries and deletions in the candidate table are needed.