Performance Measurement of Cloud Service Suppliers and Cloud Supply Chain

Azadi, Majid

Performance Measurement of Cloud Service Suppliers and Cloud Supply Chain

Azadi, Majid

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Azadi, Majid
dc.date.accessioned	2021-05-19T01:13:51Z
dc.date.available	2021-05-19T01:13:51Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/148948
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Over the past decade, cloud computing has received much consideration from both practitioners and academics. Nonetheless, the rapid increase in the industrial use-cases of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in performance measurement and the selection of the optimal CSPs according to quality of service (QoS) requirements. To date, there is no reliable approach for the performance assessment of CSPs and quantitative models are rarely used to support such decisions. In addition, the existing approaches to CSP performance measurement suffer from several limitations and drawbacks such as requiring complex calculations, being effort-intensive and being time-consuming. Furthermore, the existing approaches are unable to find slight differences between CSPs in a cloud marketplace owing to the high level of competition. These limitations are major obstacles to applying the existing approaches to assess CSPs. To address these issues in the existing literature, the objective of this study is to develop performance evaluation models based on the Data Envelopment Analysis (DEA) approach that can act as a proxy for many conventional performance assessment problems in cloud computing. The obtained results show that the models proposed in this study are extremely effective in measuring the efficiency of CSPs. Furthermore, the proposed models can deal with different types of data simultaneously in the efficiency measurement of CSPs and cloud supply chains, giving decision makers the ability to estimate the performance of CSPs efficiently.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/148948/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Performance Measurement of Cloud Service Suppliers and Cloud Supply Chain	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Over the past decade, cloud computing has received much consideration from both practitioners and academics. Nonetheless, the rapid increase in the industrial use-cases of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in performance measurement and the selection of the optimal CSPs according to quality of service (QoS) requirements. To date, there is no reliable approach for the performance assessment of CSPs and quantitative models are rarely used to support such decisions. In addition, the existing approaches to CSP performance measurement suffer from several limitations and drawbacks such as requiring complex calculations, being effort-intensive and being time-consuming. Furthermore, the existing approaches are unable to find slight differences between CSPs in a cloud marketplace owing to the high level of competition. These limitations are major obstacles to applying the existing approaches to assess CSPs. To address these issues in the existing literature, the objective of this study is to develop performance evaluation models based on the Data Envelopment Analysis (DEA) approach that can act as a proxy for many conventional performance assessment problems in cloud computing. The obtained results show that the models proposed in this study are extremely effective in measuring the efficiency of CSPs. Furthermore, the proposed models can deal with different types of data simultaneously in the efficiency measurement of CSPs and cloud supply chains, giving decision makers the ability to estimate the performance of CSPs efficiently.

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http://hdl.handle.net/10453/148948