Towards Reproducible eScience in the Cloud

Abstract

Whether it be data from ubiquitous devices such as sensors or data generated from telescopes or other laboratory instruments, technology apparent in many scientific disciplines is generating data at rates never witnessed before. Computational scientists are among the many who perform inductive experiments and analyses on these data with the goal of answering scientific questions. These computationally demanding experiments and analyses have become a common occurrence, resulting in a shift in scientific discovery, and thus leading to the term eScience. To perform eScience experiments and analysis at scale, one must have an infrastructure with enough computing power and storage space. The advent of cloud computing has allowed infrastructures and platforms to be created with theoretical limitless bounds, thus providing an attractive solution to this need. In this work, we create a reproducible process for the construction of eScience computing environments on top of cloud computing infrastructures. Our solution separates the construction of these environments into two distinct layers: (1) the infrastructure layer and (2) the software layer. We provide results of running our framework on two different computational clusters within two separate cloud computing environments to demonstrate that our framework can facilitate the replication or extension of an eScience experiment.