Efficient QoS-Aware Service Recommendation for Multi-Tenant Service-Based Systems in Cloud

Wang, Y; He, Q; Zhang, X; Ye, D; Yang, Y

Efficient QoS-Aware Service Recommendation for Multi-Tenant Service-Based Systems in Cloud

Wang, Y He, Q Zhang, X Ye, D

Yang, Y

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2020, 13, (6), pp. 1045-1058
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	He, Q
dc.contributor.author	Zhang, X
dc.contributor.author	Ye, D https://orcid.org/0000-0002-7561-0992
dc.contributor.author	Yang, Y
dc.date.accessioned	2021-03-30T03:46:03Z
dc.date.available	2021-03-30T03:46:03Z
dc.date.issued	2020-11-01
dc.identifier.citation	IEEE Transactions on Services Computing, 2020, 13, (6), pp. 1045-1058
dc.identifier.issn	1939-1374
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/147651
dc.description.abstract	© 2008-2012 IEEE. The popularity of cloud computing has fueled the growth in multi-Tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-Tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-Aware service selection at build-Time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-Tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-Tenant SBSs, one for build-Time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-Tenant SBS construction and rapid system adaptation.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2017.2761346
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	Efficient QoS-Aware Service Recommendation for Multi-Tenant Service-Based Systems in Cloud
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-30T03:46:01Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	6

Abstract:

© 2008-2012 IEEE. The popularity of cloud computing has fueled the growth in multi-Tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-Tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-Aware service selection at build-Time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-Tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-Tenant SBSs, one for build-Time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-Tenant SBS construction and rapid system adaptation.

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http://hdl.handle.net/10453/147651