Conference paper
Managing Event Oriented Workflows
This paper introduces an event-driven solution for modern scientific workflows. This novel approach enables truly dynamic workflows by splitting them into their constituent parts, defined using combinations of Patterns and Recipes, and lacking any meaningful inter-dependencies. The theory behind this system is set out, and an example workflow is presented.
A python package mig_meow, which implements this workflow system is also shown and explained. The use cases of various user groups are considered to asses the feasibility of the design, and it is found to be sufficient, especially in light of recent workflow requirements for dynamic looping, optional outputs and in-the-loop interactions.
Language: | English |
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Publisher: | IEEE |
Year: | 2020 |
Pages: | 23-28 |
Proceedings: | 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing |
Series: | Proceedings of Xloop 2020: 2nd Annual Workshop on Extreme-scale Experiment-in-the-loop Computing, Held in Conjunction With Sc 2020: the International Conference for High Performance Computing, Networking, Storage and Analysis |
Journal subtitle: | 2nd Annual Workshop on Extreme-scale Experiment-in-the-loop Computing, Held in Conjunction With Sc 2020: the International Conference for High Performance Computing, Networking, Storage and Analysis |
ISBN: | 1665422823 , 1665422831 , 9781665422826 , 9781665422833 , 0738110728 and 9780738110721 |
Types: | Conference paper |
DOI: | 10.1109/XLOOP51963.2020.00009 |
ORCIDs: | 0000-0003-4262-7138 , 0000-0003-0333-4295 and Brenne, Elise Otterlei |
Adaptation models Dynamic scheduling Jupyter MiG Python Python package Runtime Space exploration Task analysis Technological innovation Tools adaptive dynamic dynamic looping dynamic workflows event driven event oriented workflow management event-driven solution formal specification in-the-loop interactions mig_meow scientific workflows software packages workflow workflow management software workflow requirements workflow system workflow, event driven, adaptive, dynamic, Jupyter, MiG