The cell-loss ratio at a given node in an ATM switch, defined as the steady-state fraction of packets of information that are lost at that node due to buffer overflow, is typically a very small quantity that is hard to estimate by simulation. Cell losses are rare events, and importance sampling is sometimes the appropriate tool in this situation. However, finding the right change of measure is generally difficult. In this article, importance sampling is applied to estimate the cell-loss ratio in an ATM switch modeled as a queuing network that is fed by several sources emitting cells according to a Markov-modulated ON/OFF process, and in which all the cells from the same source have the same destination. The change of measure is obtained via an adaptation of a heuristic proposed by Chang et al. [1994] for intree networks. The numerical experiments confirm important efficiency improvements even for large nonintree networks and a large number of sources. Experiments with different variants of the importance sampling methodology are also reported, and a number of practical issues are illustrated and discussed.

We estimate, by simulation, the cell-loss rate in an ATM switch modeled as a queueing network.Cell losses are rare events, so estimating their frequency by simulation is hard. We experiment with importance sampling as a mean of improving the simulation efficiency in that context.1.

In simulation runs which involve rare events, Importance Sampling (IS) is often used to speed up the simulation process to get the simulation result faster. This paper proposes a new application for the Importance Sampling theory: to predict the change in the network behavior when the network input changes. This paper will show, by modifying the way the Importance Sampling theory applied in rare event simulation, it is possible to calculate precisely the amount of adjustment required in the network input for the network performance to meet a predefined target.

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