MONTE CARLO SIMULATION AS A SERVICE IN THE CLOUD
Victor Chang Robert John Walters Gary Wills
In the use of MCSaaS, we propose to remove outliers to enhance the improvement in accuracy. In the process of doing so, we propose three hypotheses. We describe our rationale and steps involved to validate them. We set up three major experiments. We conform that firstly, MCSaaS with outlier removal can reduce percentage of errors to 0.1%. Secondly, MCSaaS with outlier removal is expected to have slower performance than the one without removal but is kept within 1 second difference. Thirdly, MCSaaS in the Cloud has a significant performance improvement over the Gaussian Copula on Desktop. We describe the architecture of deployment, together with examples and results from a proof of concept implementation which shows our approach is able to match response rates of desktop systems without making simplifying assumptions and the associated potential threat to the accuracy of the results.