  This paper addresses how the benefits of cloud-based infrastructure can be
harnessed for analytical workloads. Often the software handling analytical
workloads 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 of this research is to allow Analysts to take an analytical job that
executes on their personal workstations, and with minimum effort execute it on
cloud infrastructure and manage both the resources and the data required by the
job. If this can be facilitated gracefully, then the Analyst benefits 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 using two
parallel problems and the results obtained confirm the feasibility of employing
P2RAC for solving large-scale analytical problems on the cloud.

Distributed, Parallel, and Cluster Computing;Computational Engineering, Finance, and Science;Software Engineering