​The outage Probability (OP) of the Signal-to-Interference-plus-Noise Ratio (SINR) is an important metric used to evaluate the performance of wireless communication systems operating over fading channels. One major difficulty toward assessing the OP is that, in most of the realistic scenarios, closedform expressions cannot be derived. This is for instance the case of Log-normal fading environments, in which evaluating the OP of the SINR amounts to computing the probability that a sum of correlated Log-normal variates exceeds a given threshold. Since such a probability is not known to admit a closed-form expression, it has thus far been evaluated by several approximation techniques, the accuracies of which are unfortunately not guaranteed in the interesting region of small outage probabilities. For these regions, simulation techniques based on variance reduction algorithms can represent a good alternative, being well-recognized to be quick and highly accurate for estimating rare event probabilities. This constitutes the major motivation behind our work. More specifically, we propose an efficient Importance Sampling (IS) approach which is based on a Covariance Matrix Scaling (CMS) technique and illustrate its computational gain over naive Monte Carlo (MS) simulations through some selected simulation results.