Managing Event Oriented Workflows

David Marchant; Rasmus Munk; Elise Otterlei Brenne; Brian Vinter

doi:10.1109/XLOOP51963.2020.00009

Managing Event Oriented Workflows

David Marchant, Rasmus Munk, Elise Otterlei Brenne, Brian Vinter

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

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.

Original language	English
Title of host publication	2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)
Publisher	IEEE
Publication date	2020
Pages	23-28
ISBN (Electronic)	978-0-7381-1072-1
DOIs	https://doi.org/10.1109/XLOOP51963.2020.00009
Publication status	Published - 2020
Event	XLOOP 2020 : 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing - , United States Duration: 12 Nov 2020 → 12 Nov 2020 Conference number: 2 https://wordpress.cels.anl.gov/xloop-2020

Conference

Conference	XLOOP 2020 : 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing
Number	2
Country/Territory	United States
Period	12/11/2020 → 12/11/2020
Internet address	https://wordpress.cels.anl.gov/xloop-2020

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@inproceedings{8edc0749eb55403a945678112724fb37,

title = "Managing Event Oriented Workflows",

abstract = "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.",

author = "David Marchant and Rasmus Munk and Brenne, {Elise Otterlei} and Brian Vinter",

year = "2020",

doi = "10.1109/XLOOP51963.2020.00009",

language = "English",

pages = "23--28",

booktitle = "2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)",

publisher = "IEEE",

address = "United States",

note = "XLOOP 2020 : 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing ; Conference date: 12-11-2020 Through 12-11-2020",

url = "https://wordpress.cels.anl.gov/xloop-2020",

}

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AU - Marchant, David

AU - Munk, Rasmus

AU - Brenne, Elise Otterlei

AU - Vinter, Brian

N1 - Conference code: 2

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1109/XLOOP51963.2020.00009

DO - 10.1109/XLOOP51963.2020.00009

M3 - Article in proceedings

SP - 23

EP - 28

BT - 2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)

PB - IEEE

T2 - XLOOP 2020 : 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing

Y2 - 12 November 2020 through 12 November 2020

ER -

Managing Event Oriented Workflows

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