An MCDM method for cloud service selection using a Markov chain and the best-worst method

Nawaz, F; Asadabadi, MR; Janjua, NK; Hussain, OK; Chang, E; Saberi, M

An MCDM method for cloud service selection using a Markov chain and the best-worst method

Nawaz, F Asadabadi, MR Janjua, NK Hussain, OK Chang, E Saberi, M

Permalink

Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2018, 159 pp. 120 - 131
Issue Date:: 2018-11-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (1.61 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Nawaz, F	en_US
dc.contributor.author	Asadabadi, MR	en_US
dc.contributor.author	Janjua, NK	en_US
dc.contributor.author	Hussain, OK	en_US
dc.contributor.author	Chang, E	en_US
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.date.available	2020-11-01T18:04:49Z
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	Knowledge-Based Systems, 2018, 159 pp. 120 - 131	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133036
dc.description.abstract	© 2018 Elsevier B.V. Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2018.06.010	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An MCDM method for cloud service selection using a Markov chain and the best-worst method	en_US
dc.type	Journal Article
utslib.citation.volume	159	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	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	159	en_US

Abstract:

© 2018 Elsevier B.V. Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services.

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

http://hdl.handle.net/10453/133036