Supporting consumer decisions in cloud computing using trust model

Raza, Muhammad

Supporting consumer decisions in cloud computing using trust model

Raza, Muhammad

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Raza, Muhammad
dc.date.accessioned	2022-04-13T01:42:29Z
dc.date.available	2022-04-13T01:42:29Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/156172
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	In recent years, cloud computing has gained much attention in research. Customers are reluctant to adopt cloud computing because of the ongoing issues such as data security, privacy and highly abstract nature of the cloud services. Trust can play an important role in bringing the consumers closer to cloud computing as it has been successfully implemented in several domains. The complex nature of cloud services specially, the Software as a Service (SaaS), brings new challenges for trust modelling compared to the trust models in other domains. Trust is multi-faceted, dynamic and is based on multiple factors. Besides cloud service providers willingness, the existing literature does not consider the SaaS architectures and their dependencies in their trust models. Computing trust in SaaS requires the information regarding all the trust factors. There is no approach in the existing literature to address the issue of missing information in the trust factors. Furthermore, the existing literature has not focused on forecasting the trust factors for the future time spots which is important in boosting customers confidence in the adoption of SaaS. The computation of trust in SaaS must adopt to time and should be reliable. The existing trust models for cloud services do not provide comprehensive solution that captures both dynamicity and reliability of trust factors. In this thesis, I propose and develop a comprehensive trust management framework for SaaS. The proposed framework identifies the key trust factors from SaaS architectural best practices. Each component of the framework is designed to address the issues faced by trust models for SaaS. In the first component of the framework, an ensemble machine learning approach automatically categorizes and extracts the trust factors from SaaS customer reviews. An intelligent Threshold-based Nearest Neighbour (T-NN) method is proposed to impute the missing customer sentiments in SaaS service factors. The third component of the framework is designed to forecast the values of the SaaS service factors. The forecast model is designed through a comprehensive study of the traditional and latest forecasting approaches. Finally, the last component of the framework, models the trust using an intelligent and dynamically weighted fuzzy trust model. Each component of the proposed trust management framework is validated against several well-known approaches during experiments. This thesis will help and facilitate cloud services consumers in their decisions before utilizing cloud services and also the SaaS providers to improve different aspects of their offered services.	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/10453/156172/2/02whole.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	Supporting consumer decisions in cloud computing using trust model	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

In recent years, cloud computing has gained much attention in research. Customers are reluctant to adopt cloud computing because of the ongoing issues such as data security, privacy and highly abstract nature of the cloud services. Trust can play an important role in bringing the consumers closer to cloud computing as it has been successfully implemented in several domains. The complex nature of cloud services specially, the Software as a Service (SaaS), brings new challenges for trust modelling compared to the trust models in other domains. Trust is multi-faceted, dynamic and is based on multiple factors. Besides cloud service providers willingness, the existing literature does not consider the SaaS architectures and their dependencies in their trust models. Computing trust in SaaS requires the information regarding all the trust factors. There is no approach in the existing literature to address the issue of missing information in the trust factors. Furthermore, the existing literature has not focused on forecasting the trust factors for the future time spots which is important in boosting customers confidence in the adoption of SaaS. The computation of trust in SaaS must adopt to time and should be reliable. The existing trust models for cloud services do not provide comprehensive solution that captures both dynamicity and reliability of trust factors. In this thesis, I propose and develop a comprehensive trust management framework for SaaS. The proposed framework identifies the key trust factors from SaaS architectural best practices. Each component of the framework is designed to address the issues faced by trust models for SaaS. In the first component of the framework, an ensemble machine learning approach automatically categorizes and extracts the trust factors from SaaS customer reviews. An intelligent Threshold-based Nearest Neighbour (T-NN) method is proposed to impute the missing customer sentiments in SaaS service factors. The third component of the framework is designed to forecast the values of the SaaS service factors. The forecast model is designed through a comprehensive study of the traditional and latest forecasting approaches. Finally, the last component of the framework, models the trust using an intelligent and dynamically weighted fuzzy trust model. Each component of the proposed trust management framework is validated against several well-known approaches during experiments. This thesis will help and facilitate cloud services consumers in their decisions before utilizing cloud services and also the SaaS providers to improve different aspects of their offered services.

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