Understanding Storage Trade-offs for Containerized Dataflows Across the Computing Continuum - Ferreira, Wesley; Viana, Luiz; Natan, Lucas; Kunstmann, Liliane; de Oliveira, Daniel (Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
Dataflows model and execute workloads in scientific and data-intensive domains by representing applications as tasks linked by data dependencies. This structure supports scheduling, execution, and reproducibility. Many dataflows process large datasets in distributed environments such as HPC clusters and cloud platforms. However, tasks often have heterogeneous computational and data requirements, motivating the use of the computing continuum, where workloads migrate across environments. Containers enable portable execution across heterogeneous infrastructures, but storage strategies for data sharing remain insufficiently explored. This paper evaluates multiple storage approaches, analyzing how data size, granularity, and role influence performance and efficiency.