Calculation of performance metrics such as steady-state probabilities and response time distributions in large Markov and semi-Markov models can be accomplished using parallel implementations of well-known numerical techniques. In the past these implementations have usually been run on dedicated computational clusters and networks of workstations, but the recent rise of cloud computing offers an alternative environment for executing such applications. It is important, however, to understand what effect moving to a cloud-based infrastructure will have on the performance of the analysis tools themselves. In this paper we investigate the scalability of two existing parallel performance analysis tools (one based on Laplace transform inversion and the other on uniformisation) on Amazon's Elastic Compute Cloud, and compare this with their performance on traditional dedicated hardware. This provides insight into whether such tools can be used effectively in a cloud environment, and suggests factors which must be borne in mind when designing next-generation performance tools specifically for the cloud.
Information from pubs.doc.ic.ac.uk/ukpew2010-ec2.