Electronic Thesis/Dissertation


A Model-Based Framework for Analyzing Cloud Service Provider Trustworthiness and Predicting Cloud Service Level Agreement Performance Open Access

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Analytics firm Cyence estimated Amazon’s four-hour cloud computing outage on February 28, 2017 “cost S&P; 500 companies at least $150 million” (Condliffe 2017) and traffic monitoring firm Apica claimed “54 of the top 100 online retailers saw site performance slump by at least 20 percent” (Condliffe 2017). 2015 data center outages cost Fortune 1000 companies between $1.25 and $2.5 billion (Ponemon 2017). Despite potential risks, the cloud computing industry continues to grow. For example, Internet of Things, which is projected to grow 266% between 2013 and 2020 (MacGillivray et al. 2017), will drive increased demand and dependency on cloud computing as data across multiple industries is collected and sent back to cloud data centers for processing. Enterprises continue to increase demand and dependency with 85% having multi-cloud strategies, up from 2016 (RightScale 2017a). This growth and dependency will influence risk exposure and potential for impact (e.g. availability, reliability, performance, security, financial). The research in this Praxis and proposed solution focuses on calculating cloud service provider (CSP) trustworthiness based on cloud service level agreement (SLA) criteria and predicting cloud SLA availability performance for cloud computing services. Evolving industry standards for cloud SLAs (EC 2014, Hunnebeck et al. 2011, ISO/IEC 2016, NIST 2015, Hogben, Giles and Dekker 2012) and existing work regarding CSP trustworthiness (Ghosh, Ghosh and Das 2015, Taha et al. 2014) will be leveraged as the predictive model (using Linear Regression Analysis) is constructed to analyze CSP cloud computing service, SLA performance and CSP trustworthiness.

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