  As the amount of available data continues to grow in fields as diverse as
bioinformatics, physics, and remote sensing, the importance of scientific
workflows in the design and implementation of reproducible data analysis
pipelines increases. When developing workflows, resource requirements must be
defined for each type of task in the workflow. Typically, task types vary
widely in their computational demands because they are simply wrappers for
arbitrary black-box analysis tools. Furthermore, the resource consumption for
the same task type can vary considerably as well due to different inputs. Since
underestimating memory resources leads to bottlenecks and task failures,
workflow developers tend to overestimate memory resources. However,
overprovisioning of memory wastes resources and limits cluster throughput.
  Addressing this problem, we propose Sizey, a novel online memory prediction
method for workflow tasks. During workflow execution, Sizey simultaneously
trains multiple machine learning models and then dynamically selects the best
model for each workflow task. To evaluate the quality of the model, we
introduce a novel resource allocation quality (RAQ) score based on memory
prediction accuracy and efficiency. Sizey's prediction models are retrained and
re-evaluated online during workflow execution, continuously incorporating
metrics from completed tasks.
  Our evaluation with a prototype implementation of Sizey uses metrics from six
real-world scientific workflows from the popular nf-core framework and shows a
median reduction in memory waste over time of 24.68% compared to the respective
best-performing state-of-the-art baseline.
