Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cpe Accelerating R-based Analytics on the Cloud†

This paper addresses the problem of harnessing cloud-based infrastructure for the kind of analytical workloads that abound in application domains ranging from computational finance and risk analytics to engineering and manufacturing. Often in this setting, software is not developed by a professional programmer, but on an ad hoc basis by Analysts in high-level programming environments such as R or Matlab. The goal is to allow Analysts to take an analytical job, including both the software and associated data that executes on their personal workstations, and with minimum effort execute the software on a large-scale parallel cloud infrastructure and manage both the resources and the data required by the job. If this can be facilitated gracefully, then the Analyst can not only benefit from experimenting with large-scale analytical problems in less time but also from on-demand resources, low maintenance cost and scalability of computing resources, all of which are offered by the cloud. In this paper, a Platform for Parallel R-based Analytics on the Cloud (P2RAC) that is placed between an Analyst and a cloud infrastructure is proposed and implemented. P2RAC offers a set of command-line tools for managing the resources, such as instances and clusters, the data and the execution of the software on the Amazon Elastic Computing Cloud infrastructure. Experimental studies are pursued on two parallel problems and the results obtained confirm the feasibility of employing P2RAC for solving large-scale analytica