A hybrid fuzzy framework for Cloud service selection

Le, S; Dong, H; Hussain, FK; Hussain, OK; Ma, J; Zhang, Y

A hybrid fuzzy framework for Cloud service selection

Le, S Dong, H Hussain, FK

Hussain, OK Ma, J Zhang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2014 IEEE International Conference on Web Services, ICWS 2014, 2014, pp. 313 - 320
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Le, S	en_US
dc.contributor.author	Dong, H	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Hussain, OK	en_US
dc.contributor.author	Ma, J	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings - 2014 IEEE International Conference on Web Services, ICWS 2014, 2014, pp. 313 - 320	en_US
dc.identifier.isbn	9781479950546	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121600
dc.description.abstract	© 2014 IEEE. QoS-based service rating has made positive contributions to the area of service selection. Especially for Cloud service users, the right decision when choosing suitable Cloud services can help them improve user satisfaction and trading revenues. This work aims to address the issue of uncertainty in service requests, service descriptions, user and expert preferences, as well as evaluation criteria in a MCDM-based service selection procedure. A hybrid fuzzy framework for Cloud service selection is proposed, addressing the challenge using three approaches: a fuzzy-ontology-based approach for function matching and service filtering; a fuzzy AHP technique for informed criterion weighting; and, a fuzzy TOPSIS approach for service ranking.	en_US
dc.relation.ispartof	Proceedings - 2014 IEEE International Conference on Web Services, ICWS 2014	en_US
dc.relation.isbasedon	10.1109/ICWS.2014.53	en_US
dc.title	A hybrid fuzzy framework for Cloud service selection	en_US
dc.type	Conference Proceeding
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. QoS-based service rating has made positive contributions to the area of service selection. Especially for Cloud service users, the right decision when choosing suitable Cloud services can help them improve user satisfaction and trading revenues. This work aims to address the issue of uncertainty in service requests, service descriptions, user and expert preferences, as well as evaluation criteria in a MCDM-based service selection procedure. A hybrid fuzzy framework for Cloud service selection is proposed, addressing the challenge using three approaches: a fuzzy-ontology-based approach for function matching and service filtering; a fuzzy AHP technique for informed criterion weighting; and, a fuzzy TOPSIS approach for service ranking.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121600