An efficient performance extrapolation for queuing models in transient analysis

In designing, analyzing and operating real-life complex systems, we are interested, however, not only in performance evaluation but in sensitivity analysis and optimization as well. Since most systems of practical interest are too complex to allow the analytical solution of totally realistic models, these systems must be studied by means of Monte-Carlo simulation. One problem with Monte Carlo analysis is its expensive use of computer time. To address this problem, we propose an efficient technique for estimating the expected performance of a stochastic system for various values of the parameters from a single simulation of the nominal system. This technique is based on the likelihood ratio performance extrapolation (LRPE). We provide numerical experiments that demonstrate how the proposed technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis