AI-driven Service Broker for Simple and Composite Cloud SaaS Selection

Ikram, Mohammed Abdulaziz

AI-driven Service Broker for Simple and Composite Cloud SaaS Selection

Ikram, Mohammed Abdulaziz

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Publication Type:: Thesis
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Ikram, Mohammed Abdulaziz
dc.date.accessioned	2020-08-26T05:53:21Z
dc.date.available	2020-08-26T05:53:21Z
dc.date.issued	2020
dc.identifier.uri	http://hdl.handle.net/10453/142382
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Cloud Software as a Service (SaaS) is one of the three types of services offered in cloud computing. Cloud SaaS is a software application that runs on top of Platform as a Service (PaaS), which in turn works on top of Infrastructure as a Service (IaaS). Due to the numerous advantages offered by cloud SaaS to service consumers, such as reducing the cost of IT expenditures, security capabilities and disaster recovery offered by cloud SaaS service providers, Cloud SaaS is becoming a leading and growing type of cloud service among other cloud services (i.e., IaaS and PaaS). Therefore, Cloud SaaS service consumers may face a difficult task when searching for the most suitable service based on their preferences. Service selection is based on matching the service requirements of functional and non-functional quality attributes. However, selecting a Cloud SaaS service provider with a high number of non-functional quality attributes that fulfils consumer requirements within a large number of similar functional services is a key factor for a Cloud SaaS service selection. In addition, considering that a cloud SaaS service can involve a long-term contract, Cloud SaaS providers frequently offer a free trial period to test and evaluate services before the consumers make the decision of whether they will use that service. Furthermore, selecting multiple Cloud SaaS service providers in order to create a new business value, known as a service composition in the service-oriented architecture (SOA) model, is very important, since Cloud SaaS services are the first option for deploying IT services for many new enterprises. Therefore, this research aims to propose intelligent methods for a simple and composite service selection framework based on consumer preferences. By simple, we mean a singular service whereas by composite, we mean an aggregated service. This work seeks to find the services with a high number of non-functional quality attributes that meet the consumer requirements. To achieve the objectives of this research, a design science research methodology will be adopted. Fuzzy logic will be proposed to address the uncertainty of consumer preferences. A ranking service system, evaluation system and composite decision maker system are proposed in this thesis to help a Cloud SaaS service consumer select the optimal service required. Multiple approaches of decision-makers will be developed in order to achieve our research objectives. It is expected that this research work will enhance the selection mechanism of Cloud SaaS, either simple or composite based on service consumer’s preferences.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/142382/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
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	info:eu-repo/semantics/openAccess
dc.title	AI-driven Service Broker for Simple and Composite Cloud SaaS Selection	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Cloud Software as a Service (SaaS) is one of the three types of services offered in cloud computing. Cloud SaaS is a software application that runs on top of Platform as a Service (PaaS), which in turn works on top of Infrastructure as a Service (IaaS). Due to the numerous advantages offered by cloud SaaS to service consumers, such as reducing the cost of IT expenditures, security capabilities and disaster recovery offered by cloud SaaS service providers, Cloud SaaS is becoming a leading and growing type of cloud service among other cloud services (i.e., IaaS and PaaS). Therefore, Cloud SaaS service consumers may face a difficult task when searching for the most suitable service based on their preferences. Service selection is based on matching the service requirements of functional and non-functional quality attributes. However, selecting a Cloud SaaS service provider with a high number of non-functional quality attributes that fulfils consumer requirements within a large number of similar functional services is a key factor for a Cloud SaaS service selection. In addition, considering that a cloud SaaS service can involve a long-term contract, Cloud SaaS providers frequently offer a free trial period to test and evaluate services before the consumers make the decision of whether they will use that service. Furthermore, selecting multiple Cloud SaaS service providers in order to create a new business value, known as a service composition in the service-oriented architecture (SOA) model, is very important, since Cloud SaaS services are the first option for deploying IT services for many new enterprises. Therefore, this research aims to propose intelligent methods for a simple and composite service selection framework based on consumer preferences. By simple, we mean a singular service whereas by composite, we mean an aggregated service. This work seeks to find the services with a high number of non-functional quality attributes that meet the consumer requirements. To achieve the objectives of this research, a design science research methodology will be adopted. Fuzzy logic will be proposed to address the uncertainty of consumer preferences. A ranking service system, evaluation system and composite decision maker system are proposed in this thesis to help a Cloud SaaS service consumer select the optimal service required. Multiple approaches of decision-makers will be developed in order to achieve our research objectives. It is expected that this research work will enhance the selection mechanism of Cloud SaaS, either simple or composite based on service consumer’s preferences.

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