  Many scientific workflow scheduling algorithms need to be informed about task
runtimes a-priori to conduct efficient scheduling. In heterogeneous cluster
infrastructures, this problem becomes aggravated because these runtimes are
required for each task-node pair. Using historical data is often not feasible
as logs are typically not retained indefinitely and workloads as well as
infrastructure changes. In contrast, online methods, which predict task
runtimes on specific nodes while the workflow is running, have to cope with the
lack of example runs, especially during the start-up.
  In this paper, we present Lotaru, a novel online method for locally
estimating task runtimes in scientific workflows on heterogeneous clusters.
Lotaru first profiles all nodes of a cluster with a set of short-running and
uniform microbenchmarks. Next, it runs the workflow to be scheduled on the
user's local machine with drastically reduced data to determine important task
characteristics. Based on these measurements, Lotaru learns a Bayesian linear
regression model to predict a task's runtime given the input size and finally
adjusts the predicted runtime specifically for each task-node pair in the
cluster based on the micro-benchmark results. Due to its Bayesian approach,
Lotaru can also compute robust uncertainty estimates and provides them as an
input for advanced scheduling methods.
  Our evaluation with five real-world scientific workflows and different
datasets shows that Lotaru significantly outperforms the baselines in terms of
prediction errors for homogeneous and heterogeneous clusters.
