Performance analysis of concurrent workflows
Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improvement process and the foundation of designing new workflows. This paper introduces the conc...
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| Published in | Journal of big data Vol. 2; no. 1; pp. 1 - 14 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
03.07.2015
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2196-1115 2196-1115 |
| DOI | 10.1186/s40537-015-0017-0 |
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| Abstract | Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improvement process and the foundation of designing new workflows. This paper introduces the concept of process evolution functions and event reduction policies, which allow for the time resolved visualization of an unlimited number of concurrent workflows by means of aggregated task views. The visualization allows for an intuitive approach to the performance analysis of concurrent workflows. The theoretical foundation of this approach is applicable for workflows represented by directed acyclic graphs. It is explained on the basis of a simple IO-workflow model, which is typically found for distributed resource management systems utilized for many-task computing.
AMS subject classification
68Mxx |
|---|---|
| AbstractList | Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improvement process and the foundation of designing new workflows. This paper introduces the concept of process evolution functions and event reduction policies, which allow for the time resolved visualization of an unlimited number of concurrent workflows by means of aggregated task views. The visualization allows for an intuitive approach to the performance analysis of concurrent workflows. The theoretical foundation of this approach is applicable for workflows represented by directed acyclic graphs. It is explained on the basis of a simple IO-workflow model, which is typically found for distributed resource management systems utilized for many-task computing.
AMS subject classification
68Mxx Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improvement process and the foundation of designing new workflows. This paper introduces the concept of process evolution functions and event reduction policies, which allow for the time resolved visualization of an unlimited number of concurrent workflows by means of aggregated task views. The visualization allows for an intuitive approach to the performance analysis of concurrent workflows. The theoretical foundation of this approach is applicable for workflows represented by directed acyclic graphs. It is explained on the basis of a simple IO-workflow model, which is typically found for distributed resource management systems utilized for many-task computing. AMS subject classification68Mxx |
| ArticleNumber | 10 |
| Author | Kempa-Liehr, Andreas |
| Author_xml | – sequence: 1 givenname: Andreas surname: Kempa-Liehr fullname: Kempa-Liehr, Andreas email: kempa-liehr@fmf.uni-freiburg.de organization: Freiburg Materials Research Center, University of Freiburg, EnBW Energie Baden-Württemberg AG, Blue Yonder GmbH |
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| Cites_doi | 10.1023/A:1008685117587 10.1145/1667062.1667063 10.1007/BFb0053710 10.1007/978-3-642-30715-7 10.1023/A:1019092506798 10.1109/HPDC.2005.1520947 10.1016/j.future.2007.01.003 10.1007/11847366_18 10.1016/j.future.2010.07.002 10.1109/TVCG.2009.187 10.1016/j.future.2008.02.008 10.1007/978-3-642-17616-6_6 10.1016/j.future.2011.04.005 10.1145/359545.359563 10.1016/j.future.2011.06.008 10.1109/MTAGS.2008.4777912 10.1016/j.future.2008.06.012 10.1147/sj.41.0025 10.1109/CCGRID.2009.59 10.1109/32.391378 10.1109/TPDS.2011.66 10.1147/sj.444.0821 10.1109/PACIFICVIS.2008.4475466 10.1007/BFb0053711 10.1109/IPDPSW.2012.213 10.1016/j.future.2004.07.002 10.1016/j.websem.2004.03.001 10.1016/j.future.2010.11.003 10.1002/9781119198697 |
| ContentType | Journal Article |
| Copyright | Kempa-Liehr; licensee Springer. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Journal of Big Data is a copyright of Springer, (2015). All Rights Reserved. |
| Copyright_xml | – notice: Kempa-Liehr; licensee Springer. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. – notice: Journal of Big Data is a copyright of Springer, (2015). All Rights Reserved. |
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| SubjectTerms | Automation Big Data Communications Engineering Computational Science and Engineering Computer Science Continuous improvement Data management Data Mining and Knowledge Discovery Database Management Graph theory Graphical representations Information Storage and Retrieval Management systems Mathematical Applications in Computer Science Networks Pipelines Resource management Visualization Workflow |
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| Title | Performance analysis of concurrent workflows |
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