Work

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

Year: 2021

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2110.13999

Authors: Nouri, Azita, Davis, Philip E., Subedi, Pradeep, Parashar, Manish

Institutions: Rutgers, The State University of New Jersey, University of Utah

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W3211060193

Cites: 138

Cited by: 7

Related to: 10