Computational workflows need to retain data from both intermediate stages and final results to ensure the reproducibility and trustworthiness of scientific discoveries. While cloud infrastructure offers advantages like elasticity and automation, it compromises the persistence of intermediate data to ensure performance and reduce costs. Utilizing node-local storage can enhance performance but requires manual data transfers to persistent storage, making the technique impractical. To address these challenges, we propose a software architecture called Persistent, Shared, and Scalable Data (PerSSD) that integrates cloud operators and a Network File System (NFS) to make node-local data persistent and shareable across cloud nodes while ensuring performance. PerSSD outperforms traditional cloud object storage, achieving 35% reduction in the overall execution time of an earth science workflow, all while ensuring data persistence and shareability.

Our poster presents a data analytics strategy to enable scientists to model patterns of soil moisture data at different resolutions across the United States. We build upon previous work of Guevara and co-authors with three contributions. First, we introduce divisions of soil moisture into the climatic regions proposed by the National Ecology Observatory Network. Second, we reduce the topological parameters used in modeling soil moisture using Principal Component Analysis. Third, we present an efficient workflow for modeling and visualizing soil moisture data.