Biomedical research in the Cloud:
Options and factors for researchers and organizations considering
moving to (or adding) cloud computing resources
As biomedical research data grow, researchers need reliable and scalable solutions for storage and compute. Many large research organizations are moving to the cloud to handle biocomputational research, including NIH, NSF, and many academic research institutions. There is also a growth in funding opportunities for Machine Learning/Artificial Intelligence (ML/AI) research, as well as a focus on developing public policies for ML/AI research. These types of research efforts often require large compute and/or supercomputing, beyond what is available to many students on their own laptops. For researchers at institutions who do not have access to large on premise computation and/or supercomputers, the cloud can be a good option to enable research on larger scales. Additionally, the ability to use tools for ML/AI, such as TensorFlow, can enable researchers to get the most out of their data.
When evaluating the possibility of using cloud for research, researchers and organizational IT professionals often consider the cost, size, and age of on-premise infrastructure, familiarity and ability to implement cloud-based systems, as well as the research-specific factors like size and persistence of data sets, frequency of use, types of analysis workflows, and bioinformatics tools and languages. The choice of which cloud(s) to use often also involves cost comparison and an evaluation of which tools are available on the various cloud platforms. Peculiarities of the academic research environment, including especially funding models, complicate the decision about whether to migrate to cloud computing. There is also an ability for organizations to create multi-cloud and hybrid solutions so that the cloud can be used to extend on-premise environments, act as a bridge to cloud computing, and/or enable choice among researchers as to which cloud platform to use. This flexibility means that there are a wide variety of options available, which can also make the decision more confusing and the path forward less clear.
The goal of this workshop is to explore the benefits of using the cloud in biomedical and computational research, as well as considerations (pros and cons) for a range of scenarios including individual researchers, collaborative research teams, consortia research programs, and large biomedical research agencies / organizations.
We solicit topics that will result in a balanced discussion about the pros and cons of moving to the cloud in different situations, while considering different-sized labs and organizations, as well as for a variety of different types of research. This balanced perspective is a key feature of this Workshop.
Specific topics may include (but are not limited to):
Talks can range from technical to more high-level strategic planning and administrative, and anything in between.
Submit your talk for this workshop using the Google Form here.
Michelle Holko, PhD, PMP
Principal Architect & Scientist
Google Cloud, Global Public Sector
Cloud Services Program Manager
NIH Center for Information Technology (CIT)
Steven E. Brenner
Professor, Departments of Bioengineering,
CTO, All of Us Research Program
National Institutes of Health