This research area comprises five ongoing and two completed subprojects that research novel abstractions, algorithms, and methods for the computational infrastructures where DAWs are deployed and executed. This encompasses DAW execution engines, schedulers, and resource managers. This research area is especially relevant to multi-site DAW execution.
Two of the projects are interdisciplinary collaborations between computer science and remote sensing, a field which relies on datasets too large to easily consolidate in a single location.
B1: Carbon-aware Multi-site Workflow Scheduling Under Uncertainty
B2: Portable and Adaptive Data Analysis Workflows for Real-Time 3D Vision Completed in Phase I
B3: Debugging Distributed Data Analysis Workflows Completed in Phase I
B4: Proactive Network, I/O, and Storage Steering for Multiple DAWs on Shared Infrastructures
B5: Transparent Multi-Site Data Analysis Workflows for Earth Observation
B6: End-to-end Energy Profiles of ML-based Data Analysis Workflows
B7: Efficient DAW Execution Using Incremental Data for Monitoring Forest Disturbances