A novel heuristic scheme for modeling and managing time bound constraints in data-intensive grid and cloud infrastructures

Cuzzocrea, A; Xu, G

A novel heuristic scheme for modeling and managing time bound constraints in data-intensive grid and cloud infrastructures

Cuzzocrea, A Xu, G

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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), 2014, 8842 pp. 172 - 191
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Cuzzocrea, A	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8842 pp. 172 - 191	en_US
dc.identifier.isbn	9783662455494	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33770
dc.description.abstract	© Springer-Verlag Berlin Heidelberg 2014. Inspired by the emerging Cloud Computing challenge, in this paper we provide a comprehensive framework for modeling and managing time bound constraints in data-intensive Grid and Cloud infrastructures, along with its experimental assessment and analysis. We provide both conceptual and theoretical contributions of the proposed framework, along with a heuristic scheme, called RGDTExec, that solves all possible instances of the problem underlying the proposed framework by exploiting a suitable greedy algorithm, called RGDTExecRun. As we demonstrate throughout the paper, the framework keeps several aspects of research innovations that are beneficial in a wide range of application scenarios.	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.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A novel heuristic scheme for modeling and managing time bound constraints in data-intensive grid and cloud infrastructures	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8842	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Amantea, Italy
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8842	en_US

Abstract:

© Springer-Verlag Berlin Heidelberg 2014. Inspired by the emerging Cloud Computing challenge, in this paper we provide a comprehensive framework for modeling and managing time bound constraints in data-intensive Grid and Cloud infrastructures, along with its experimental assessment and analysis. We provide both conceptual and theoretical contributions of the proposed framework, along with a heuristic scheme, called RGDTExec, that solves all possible instances of the problem underlying the proposed framework by exploiting a suitable greedy algorithm, called RGDTExecRun. As we demonstrate throughout the paper, the framework keeps several aspects of research innovations that are beneficial in a wide range of application scenarios.

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http://hdl.handle.net/10453/33770