Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification

With the advancement of technology, networking and the Internet, the Cloud computing Software as a Service (SaaS) model has changed the sales model for software providers by allowing customers to access applications with browsers and smartphones. Recently, SaaS has become the preferred delivery mode...

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Main Author:	Wong, Tong Sheng
Format:	Thesis
Published:	2019
Subjects:	QA76.75-76.765 Computer software
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id	my-mmu-ep.7716
record_format	uketd_dc
spelling	my-mmu-ep.77162020-09-17T02:48:34Z Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification 2019-07 Wong, Tong Sheng QA76.75-76.765 Computer software With the advancement of technology, networking and the Internet, the Cloud computing Software as a Service (SaaS) model has changed the sales model for software providers by allowing customers to access applications with browsers and smartphones. Recently, SaaS has become the preferred delivery model and consumers are increasingly demanding more custom-built SaaS for delivering specific business outcomes. However, the dynamic nature of Cloud computing has led to Cloud services’ such as SaaS, acceptance being slowed down. The main concern of the Cloud SaaS providers is on guaranteeing and of the Cloud consumers is on receiving quality services in terms of performance such as response time, throughput and availability as agreed in the Service Level Agreement (SLA). Thus, actions have to be taken by the Cloud SaaS providers to detect, predict, prevent and rectify such Quality of Service (QoS) violations. To overcome such a challenge, many existing research works have been in progress and focused on the resources management to minimize quality of service (QoS) violation so as to maintain the level of performance and availability guaranteed in the SLA. This thesis addresses the main concern by proposing an approach in monitoring Cloud environment resources to counter measure Cloud QoS violation. This proposed approach is named as Cloud QoS Detection, Prediction, Prevention and Rectification (CQoS-DPPR). CQoS-DPPR is embedded with 16 decision rules on response time and throughput for detection and prediction of QoS violations. Based on these rules, a scaling and fault tolerant (SFT) algorithm is activated to decide whether scaling of resources is required to prevent the cloud service to be further downgraded to certainly violation condition due to resource is not appropriately provisioned. 2019-07 Thesis http://shdl.mmu.edu.my/7716/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Computing & Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Wong, Tong Sheng Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
description	With the advancement of technology, networking and the Internet, the Cloud computing Software as a Service (SaaS) model has changed the sales model for software providers by allowing customers to access applications with browsers and smartphones. Recently, SaaS has become the preferred delivery model and consumers are increasingly demanding more custom-built SaaS for delivering specific business outcomes. However, the dynamic nature of Cloud computing has led to Cloud services’ such as SaaS, acceptance being slowed down. The main concern of the Cloud SaaS providers is on guaranteeing and of the Cloud consumers is on receiving quality services in terms of performance such as response time, throughput and availability as agreed in the Service Level Agreement (SLA). Thus, actions have to be taken by the Cloud SaaS providers to detect, predict, prevent and rectify such Quality of Service (QoS) violations. To overcome such a challenge, many existing research works have been in progress and focused on the resources management to minimize quality of service (QoS) violation so as to maintain the level of performance and availability guaranteed in the SLA. This thesis addresses the main concern by proposing an approach in monitoring Cloud environment resources to counter measure Cloud QoS violation. This proposed approach is named as Cloud QoS Detection, Prediction, Prevention and Rectification (CQoS-DPPR). CQoS-DPPR is embedded with 16 decision rules on response time and throughput for detection and prediction of QoS violations. Based on these rules, a scaling and fault tolerant (SFT) algorithm is activated to decide whether scaling of resources is required to prevent the cloud service to be further downgraded to certainly violation condition due to resource is not appropriately provisioned.
format	Thesis
qualification_level	Master's degree
author	Wong, Tong Sheng
author_facet	Wong, Tong Sheng
author_sort	Wong, Tong Sheng
title	Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
title_short	Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
title_full	Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
title_fullStr	Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
title_full_unstemmed	Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification
title_sort	support vector machine based cloud quality of service violation prediction with rule-based rectification
granting_institution	Multimedia University
granting_department	Faculty of Computing & Informatics
publishDate	2019
_version_	1747829665297334272

Support Vector Machine Based Cloud Quality Of Service Violation Prediction With Rule-Based Rectification

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