FUNCTION-AS-A-SERVICE CLOUD COMPUTING FOR LAKE WATER QUALITY FORECASTING: PAST, PRESENT AND FUTURE

Iterative, near-term water quality forecasting requires flexible cyberinfrastructure (CI) that meets several requirements. First, the CI must be reliable and support decision-making. Second, the CI must scale to support forecasting across a diverse range of waterbodies. Third, it must support complex workflows with various modules (e.g. data collection, QA/QC, ensemble execution) that are orchestrated to run autonomously. Fourth, it must be affordable and accessible to the growing community of aquatic  forecasters. The Function-as-a-Service (FaaS) cloud computing model supported by commercial and open-source platforms (e.g. Amazon Lambda, GitHub Actions, OpenWhisk) meets all of these needs. Specifically, FaaS  1) offers automation, scalability and fault-tolerance by leveraging large-scale distributed computing resources, 2) provides a high degree of flexibility and customization of computing environments through containerization (e.g. Docker), and 3) is pay-as-you-go, where charges (e.g. bills, allocation credits) are proportional to the time actually used for computing (not idle time). This presentation will: 1) overview experiences and lessons learned from a successful use case of FaaS in lake forecasting , 2) describe the development of a recently developed novel open-source software (FaaSr) which lowers the barrier to entry to use FaaS for workflows written in R, and 3) describe future directions and use cases of FaaS in aquatic ecosystem forecasting workflows.

Primary Presenter: Renato Figueiredo, University of Florida (rjofig@gmail.com)

Authors:

Renato Figueiredo, University of Florida  (renatof@ufl.edu)

Sungjae Park, University of Florida  (spark7@ufl.edu)

Cayelan Carey, Center for Ecosystem Forecasting, Virginia Tech  (cayelan@vt.edu)

R. Quinn Thomas, Center for Ecosystem Forecasting, Virginia Tech  (rqthomas@vt.edu)