Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges
arXiv:2110.13999
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.
References in corpus (10)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- End to End Learning for Self-Driving Cars
- Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
- HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures
- Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
- Keeping Track of User Steering Actions in Dynamic Workflows
- JobPruner: A Machine Learning Assistant for Exploring Parameter Spaces in HPC Applications
- Hybrid Ant Colony Algorithm Clonal Selection in the Application of the Cloud's Resource Scheduling
- Modular Resource Centric Learning for Workflow Performance Prediction
- Scalable Graph Embedding LearningOn A Single GPU