  We present a case study of a cloud-based computational workflow for
processing large astronomical data sets from the Murchison Widefield Array
(MWA) cosmology experiment. Cloud computing is well-suited to large-scale,
episodic computation because it offers extreme scalability in a pay-for-use
model. This facilitates fast turnaround times for testing computationally
expensive analysis techniques. We describe how we have used the Amazon Web
Services (AWS) cloud platform to efficiently and economically test and
implement our data analysis pipeline. We discuss the challenges of working with
the AWS spot market, which reduces costs at the expense of longer processing
turnaround times, and we explore this tradeoff with a Monte Carlo simulation.
