DaYu: Optimizing Distributed Scientific Workflows by Decoding Dataflow Semantics and Dynamics

The combination of ever-growing scientific datasets and distributed workflow complexity creates I/O performance bottlenecks due to data volume, velocity, and variety. Although the increasing use of descriptive data formats (e.g., HDF5, netCDF) helps organize these datasets, it also introduces obscure bottlenecks due to the need to translate high-level operations into file addresses and then into low-level I/O operations. To address this challenge, we introduce DaYu, a method and toolset for analyzing (a) semantic relationships between logical datasets and file addresses, (b) how dataset operations translate into I/O, and (c) the combination across entire workflows. DaYu's analysis and visualization enable the identification of critical bottlenecks and the reasoning about remediation. We describe our methodology and propose optimization guidelines. Evaluation on scientific workflows demonstrates up to a 3.7x performance improvement in I/O time for obscure bottlenecks. The time and storage overhead for DaYu's time-ordered data are typically under 0.2% of runtime and 0.25% of data volume, respectively.