International Journal of High Performance Computing Applications, Volume: 33 issue: 6, page(s): 1128-1139, 2019, doi: https://doi.org/10.1177/1094342019852127
Machine learning is being applied in a number of every day contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, machine learning is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this paper, we describe the opportunities of using machine learning in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying machine learning techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the machine learning field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.