  In this survey, we discuss the challenges of executing scientific workflows
as well as existing Machine Learning (ML) techniques to alleviate those
challenges. We provide the context and motivation for applying ML to each step
of the execution of these workflows. Furthermore, we provide recommendations on
how to extend ML techniques to unresolved challenges in the execution of
scientific workflows. Moreover, we discuss the possibility of using ML
techniques for in-situ operations. We explore the challenges of in-situ
workflows and provide suggestions for improving the performance of their
execution using ML techniques.
