FONDA PhD Defense: Fabian Lehmann on “Adaptive Scheduling of Dynamic Workflows”

Fabian Lehmann defended his dissertation, “Adaptive Scheduling of Dynamic Workflows” with distinction on June 29th, 2026. Fabian was a member of subproject B5 in FONDA Phase I. His work examines the tradeoff between workflow portability and efficiency, and introduces WONDERS, a best of both worlds optimization strategy for Nextflow workflows.

In order for scientific workflows to be portable, workflow management systems such as Nextflow cannot rely on hard-coded assumptions about the dataset or underlying infrastructure to improve efficiency. WONDERS utilizes the Common Workflow Scheduling Interface (CWSI), developed by Fabian, to bring workflow context to the resource manager, then combines his three novel approaches for workflow optimization: WOW, PONDER, and SCALE.

WOW is a scheduling approach which reduces network congestion within a cluster by basing scheduling decisions primarily on the co-location of tasks and data. PONDER and SCALE are tools which predict and minimize the ongoing use of memory (PONDER) and CPU (SCALE) over the course of a workflow execution. The combination of the three techniques in WONDERS results in super-additive improvements in workflow makespan (average reduction ~50% compared to nf-core default settings) when tested on a variety of Nextflow workflows using real-world data from remote sensing and bioinformatics.

Congratulations Fabian!

FONDA PhD Defense: Jonathan Bader on “Task Resource Prediction for Efficient Execution of Scientific Workflows”

Jonathan Bader defended his doctoral dissertation “Task Resource Prediction for Efficient Execution of Scientific Workflows” with distinction on June 4th, 2025. He is a member of the group “Distributed and Operating Systems” at TU Berlin, where he worked on FONDA subproject B1. His work focuses on predicting which tasks in a workflow are most resource intensive in order to dynamically adjust resource allocation and scheduling.

As part of this research, he introduced Lotaru and Sizey, two novel methods for predicting task run-time and memory requirements, respectively. Lotaru allows researchers to create a sensible baseline resource allocation profile for a workflow based on the task requirements and target infrastructure. Sizey continuously predicts the amount of memory each task requires and adjusts the memory allocation during runtime to minimize over-allocation while also preventing failures. Both outperform previous methods and improve the efficiency of workflow execution.

Congratulations Jonathan!