Empirical Study of Cloud Deployment Strategies: Guiding the choice between Containerization, Traditional and Hybrid Deployment

Shah, Arpit (2024) Empirical Study of Cloud Deployment Strategies: Guiding the choice between Containerization, Traditional and Hybrid Deployment. Masters thesis, Dublin, National College of Ireland.

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Abstract

The empirical study provides a comprehensive evaluation of cloud deployment strategies – containerization, traditional virtual machines (VMs), and hybrid methods for three application types like static web applications, database web application and multithreaded applications with RabbitMQ. Motivated by the need for practical, data-driven guidance for cloud practitioners, the study evaluates key metrics such as performance, scalability, cost, reliability, and operational complexity. The findings shows that containerized deployment offer better performance and scalability for static web applications, hybrid deployments excel in performance, scalability and reliability for database web applications and multithreaded applications but both deployment strategies require complex setups which increases the operational complexity. While traditional VM deployments offer easy setup and low-cost offering usability for smaller applications and applications which do not have much load like academic projects or proof of concepts. A decision tree-based recommendation tool was developed to support practitioners in selecting appropriate deployment strategies based on the empirical data. Despite some of the limitations, including short evaluation period and resource constraints on scalability tests, this study shows a direction for future research in long performance analysis, broader application types, in depth scalability test and enhancing the recommendation tool. This future work will also help in commercializing this research study by the support of recommendation tool. The research ultimately provides actionable insights and practical tools to optimize cloud deployment strategies for its users, to ensure informed decision-making based on application requirements and scenarios.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Heeney, Sean UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 10:46
Last Modified:	04 Jul 2025 10:46
URI:	https://norma.ncirl.ie/id/eprint/8053

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