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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Veröffentlicht in:Journal of grid computing Jg. 13; H. 4; S. 457 - 493
Hauptverfasser: Liu, Ji, Pacitti, Esther, Valduriez, Patrick, Mattoso, Marta
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.12.2015
Springer Nature B.V
Springer Verlag
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-015-9329-8