A Survey of Data-Intensive Scientific Workflow Management

Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution...

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Vydané v:	Journal of grid computing Ročník 13; číslo 4; s. 457 - 493
Hlavní autori:	Liu, Ji, Pacitti, Esther, Valduriez, Patrick, Mattoso, Marta
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.12.2015 Springer Nature B.V Springer Verlag
Predmet:	Clouds Computation Computational grids Computer Science Data processing Distributed, Parallel, and Cluster Computing Handles Management of Computing and Information Systems Mathematical models Parallel processing Processor Architectures User Interfaces and Human Computer Interaction Workflow Workflow management systems Workflow software Scientific workflow management system Grid Cloud Multisite cloud Scheduling Distributed and parallel data management Scientific workflow Parallelization
ISSN:	1570-7873, 1572-9184
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Abstract	Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems ( SWfMSs ) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.
AbstractList	Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud. Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems ( SWfMSs ) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.
Author	Liu, Ji Mattoso, Marta Pacitti, Esther Valduriez, Patrick
Author_xml	– sequence: 1 givenname: Ji surname: Liu fullname: Liu, Ji email: jiliuwork@gmail.com organization: MSR-Inria Joint Centre, Inria and LIRMM and University of Montpellier – sequence: 2 givenname: Esther surname: Pacitti fullname: Pacitti, Esther organization: Inria and LIRMM, University of Montpellier – sequence: 3 givenname: Patrick surname: Valduriez fullname: Valduriez, Patrick organization: MSR-Inria Joint Centre, Inria and LIRMM and University of Montpellier – sequence: 4 givenname: Marta surname: Mattoso fullname: Mattoso, Marta organization: COPPE/Federal University of Rio de Janeiro
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SubjectTerms	Clouds Computation Computational grids Computer Science Data processing Distributed, Parallel, and Cluster Computing Handles Management of Computing and Information Systems Mathematical models Parallel processing Processor Architectures User Interfaces and Human Computer Interaction Workflow Workflow management systems Workflow software
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Title	A Survey of Data-Intensive Scientific Workflow Management
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