Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds

Zheng, W; Qin, Y; Bugingo, E; Zhang, D; Chen, J

Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds

Zheng, W Qin, Y Bugingo, E Zhang, D Chen, J

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2018, 82 pp. 244 - 255
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Zheng, W	en_US
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Bugingo, E	en_US
dc.contributor.author	Zhang, D	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2018, 82 pp. 244 - 255	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131304
dc.description.abstract	© 2017 Elsevier B.V. Cloud computing has been widely regarded as a capable solution for big data processing. Nowadays cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination under a deadline constraint is becoming more complex for users. The complexity of determining the cost-minimized combination may be resulted from different causes: the characteristics of user applications, and providers’ setting on the configurations and pricing of virtual machine. In this paper, we proposed a variety of algorithms to help the users to schedule their big data processing workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithms were evaluated by extensive simulation experiments with diverse experimental settings.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2017.12.004	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Distributed Computing	en_US
dc.title	Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds	en_US
dc.type	Journal Article
utslib.citation.volume	82	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	82	en_US

Abstract:

© 2017 Elsevier B.V. Cloud computing has been widely regarded as a capable solution for big data processing. Nowadays cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination under a deadline constraint is becoming more complex for users. The complexity of determining the cost-minimized combination may be resulted from different causes: the characteristics of user applications, and providers’ setting on the configurations and pricing of virtual machine. In this paper, we proposed a variety of algorithms to help the users to schedule their big data processing workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithms were evaluated by extensive simulation experiments with diverse experimental settings.

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http://hdl.handle.net/10453/131304