An Empirical Study of the Scalability of Performance Analysis Tools in the Cloud

Calculation of performance metrics such as steadystate 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 he scalability of two existing parallel performance analysistools (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 nextgeneration performance tools specifically for the cloud. Keywords-Cloud computing; Performance analysis; Parallel computing; Empirical study;