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.
翻译：在这次调查中,我们讨论了执行科学工作流程和现有机器学习技术以缓解这些挑战的挑战,我们为在这些工作流程执行的每一步骤应用ML提供了背景和动机,此外,我们就如何将ML技术推广到科学工作流程执行方面尚未解决的挑战提出了建议,此外,我们还讨论了利用ML技术进行现场作业的可能性,我们探讨了现场工作流程的挑战,并就如何利用ML技术改进执行绩效提出了建议。
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