  In the recent years, scientific workflows gained more and more popularity. In
scientific workflows, tasks are typically treated as black boxes. Dealing with
their complex interrelations to identify optimization potentials and
bottlenecks is therefore inherently hard. The progress of a scientific workflow
depends on several factors, including the available input data, the available
computational power, and the I/O and network bandwidth. Here, we tackle the
problem of predicting the workflow progress with very low overhead. To this
end, we look at suitable formalizations for the key parameters and their
interactions which are sufficiently flexible to describe the input data
consumption, the computational effort and the output production of the
workflow's tasks. At the same time they allow for computationally simple and
fast performance predictions, including a bottleneck analysis over the workflow
runtime. A piecewise-defined bottleneck function is derived from the discrete
intersections of the task models' limiting functions. This allows to estimate
potential performance gains from overcoming the bottlenecks and can be used as
a basis for optimized resource allocation and workflow execution.
