A data dependency and access threshold based replication strategy for multi-cloud workflow applications

Xie, F; Yan, J; Shen, J

A data dependency and access threshold based replication strategy for multi-cloud workflow applications

Xie, F Yan, J Shen, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11434 LNCS pp. 281 - 293
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Xie, F	en_US
dc.contributor.author	Yan, J	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.date.available	2020-05-25T19:13:57Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11434 LNCS pp. 281 - 293	en_US
dc.identifier.isbn	9783030176419	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134390
dc.description.abstract	© Springer Nature Switzerland AG 2019. Data replication is one of the significant sub-areas of data management in cloud based workflows. Data-intensive workflow applications can gain great benefits from cloud environments and usually need data management strategies to manage large amounts of data. At the same time, multi-cloud environments become more and more popular. We propose a cost-effective and threshold-based data replication strategy with the consideration of both data dependency and data access times for data-intensive workflows in the multi-cloud environment. Finally, the simulation results show that our approach can greatly reduce total cost of data-intensive workflow applications by considering both of data dependency and data access times in multi-cloud environments.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-17642-6_24	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A data dependency and access threshold based replication strategy for multi-cloud workflow applications	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11434 LNCS	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	11434 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2019. Data replication is one of the significant sub-areas of data management in cloud based workflows. Data-intensive workflow applications can gain great benefits from cloud environments and usually need data management strategies to manage large amounts of data. At the same time, multi-cloud environments become more and more popular. We propose a cost-effective and threshold-based data replication strategy with the consideration of both data dependency and data access times for data-intensive workflows in the multi-cloud environment. Finally, the simulation results show that our approach can greatly reduce total cost of data-intensive workflow applications by considering both of data dependency and data access times in multi-cloud environments.

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http://hdl.handle.net/10453/134390