Authors: Victor Chang; Robert John Walters; Gary Wills
Addresses: School of Computing, Creative Technologies and Engineering, Leeds Beckett University, Leeds, LS1 3HE UK; Electronics and Computer Science, University of Southampton, Southampton, UK ' Electronics and Computer Science, University of Southampton, Southampton, UK ' Electronics and Computer Science, University of Southampton, Southampton, UK
Abstract: We propose a Monte Carlo simulation as a service (MCSaaS) which takes the benefits from two sides: the accuracy and reliability of typical Monte Carlo simulations and the fast performance of offering services in the Cloud. In the use of MCSaaS, we propose to remove outliers to enhance the improvement in accuracy. We propose three hypotheses and describe our rationale, architecture and steps involved for validation. We set up three major experiments. We confirm that firstly, MCSaaS with outlier removal reduces 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 one second difference. Thirdly, MCSaaS in the Cloud has a significant performance improvement over a popular model on desktop. We demonstrate our approach can meet the demands for accuracy and performance.
Keywords: Monte Carlo methods; MCM; Monte Carlo simulation as a service; MCSaaS; least squares methods; LSMs; Gaussian copula; cloud services; cloud computing.
International Journal of Business Process Integration and Management, 2015 Vol.7 No.3, pp.262 - 271
Received: 01 Nov 2013
Accepted: 29 Mar 2014
Published online: 18 Aug 2015 *