Uppsats

Designing a Framework for Evaluating the Impact of Serverless Computing on Web Application Development

Kandidat-uppsats

Blekinge Tekniska Högskola/Institutionen för datavetenskap

Publicerad: 2024

Språk: Engelska

Sammanfattning

Serverless computing is transforming web development by offering scalable and cost-effective solutions. By using mixed-method approach, correlation studies, and framework creation, the study evaluates its influence to give an understanding of operational implications and guide possible future strategies. Background: The scalability and cost-effectiveness of serverless computing revolutionize web app development. For developers to effectively use this technology, they must comprehend how it affects scalability, architecture, and operations. Objectives: The study investigates the effects of serverless computing on web app development, with a focus on operational, cost, and scalability issues. Using correlation and survey data, it examines the issues and views of developers. Method: Surveys and reviews of the literature are combined in the study to evaluate the impact of serverless computing. Python programming helps in calculating statistics and generating visualizations such as histograms, scatter plots, and the correlation matrix, which guides developers better utilize serverless technology. Results: Developers highly value scalability in serverless architectures, seeing it as essential for modern web applications. Strong correlations between deployment efficiency, security implementation, and long-term scalability emphasize the interdependence of these operational aspects in serverless environments. Conclusion: Serverless computing presents both advantages and challenges for web application development, influencing architecture, scalability, cost, and operations. Strategies to enhance efficiency, scalability, and security within serverless environments are crucial for optimizing developer experiences and outcomes.

Information

Lärosäte / institution
Blekinge Tekniska Högskola/Institutionen för datavetenskap
Publiceringsdatum
2024
Uppsatstyp
Kandidat-uppsats
Språk
Engelska

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