Summary
Background: “Cloud” computing providers, such as the Amazon Web Services (AWS), offer stable and scalable computational resources based on hardware virtualization, with short, usually hourly, billing periods. The idea of pay-as-you-use seems appealing for biometry research units which have only limited access to university or corporate data center resources or grids.
Objectives: This case study compares the costs of an existing heterogeneous on-site hardware pool in a Medical Biometry and Statistics department to a comparable AWS offer.
Methods: The “total cost of ownership”, including all direct costs, is determined for the on-site hardware, and hourly prices are derived, based on actual system utilization during the year 2011. Indirect costs, which are difficult to quantify are not included in this comparison, but nevertheless some rough guidance from our experience is given. To indicate the scale of costs for a methodological research project, a simulation study of a permutation-based statistical approach is performed using AWS and on-site hardware.
Results: In the presented case, with a system utilization of 25 –30 percent and 3 – 5-year amortization, on-site hardware can result in smaller costs, compared to hourly rental in the cloud dependent on the instance chosen. Renting cloud instances with sufficient main memory is a deciding factor in this comparison.
Conclusions: Costs for on-site hardware may vary, depending on the specific infrastructure at a research unit, but have only moderate impact on the overall comparison and subsequent decision for obtaining affordable scientific computing resources. Overall utilization has a much stronger impact as it determines the actual computing hours needed per year. Taking this into account, cloud computing might still be a viable option for projects with limited maturity, or as a supplement for short peaks in demand.
Keywords
Costs and cost analysis - biostatistics - mathematical computing - cloud computing - Amazon Web Services
