Abstract: Cloud computing represents a new way to deploy computing technology, where dynamically scalable and virtualized resources are provided as a service over the Internet. Amazon Elastic Cloud (EC2) is an example of Infrastructure-as-a-Service that anyone can use today to access infinite computing capacity on demand. This new environment enables collaboration, resources sharing and provides the tools for traceable and reproducible computational research. This model of allocating processing power holds the promise of a revolution in scientific and statistical computing.
Bringing this new era for research and education still requires new software that bridges the gap between the scientist’s everyday tools and the cloud. For instance, making R available as a service in the cloud and allowing its use without any memory or computing constraints would benefit the broad population of statisticians and research professionals. This is what Elastic–R (www.elasticr.net) delivers. It provides a Google docs-like portal and workbench for data analysis that makes using R on the cloud even simpler than using it locally. It enables scientists, educators and students to allocate cloud resources seamlessly work with R engines and use their full capabilities from within any standard web browser.
Features include real time collaboration, sharing and re-using virtual machines, sessions, data, functions, spreadsheets, dashboards, and automatically generated macro enabled Word documents and Excel workbooks which can be synchronized in real-time with R engines on the cloud. Computationally intensive algorithms can easily be run on any number of virtual machines that are controlled from within a standard R session. Elastic-R is also an applications platform that allows anyone to assemble statistical methods and data with interactive user interfaces for the end user. These interfaces and dashboards are created visually, and are automatically published and delivered as simple web applications.
In financial environments, this allows analysts to share common data sources and dashboards and to mirror them in a familiar office environment. In an industrial environment, it allows sharing data and analyses among different production and research sites which may not have the same computing environment. Finally, since the proposed computing architecture uses a cloud as a work horse, large scale and resource demanding calculations can be carried out at a on-demand basis without the need of installing high performance computing systems locally.