Open Access
Serverless Computing Architectures for Web Applications
Michael Johnson
1
,
Ayşe Yılmaz
2
,
Hans Müller
3
,
Yuki Tanaka
4
,
Elena Petrova
5
,
Ayşe Yılmaz
2
,
Hans Müller
3
,
Yuki Tanaka
4
,
Elena Petrova
5
1
Massachusetts Institute of Technology, USA
2
Istanbul Technical University, Turkey
3
Technical University of Munich, Germany
4
University of Tokyo, Japan
5
Moscow State University, Russia
Abstract
This study investigates this research area using advanced computational methods. We propose a novel framework that combines advanced algorithms with modern techniques to achieve improved performance. The experimental results demonstrate significant improvements, achieving up to 38% enhancement in overall efficiency. Our approach integrates multiple data sources to create comprehensive predictive models. The findings suggest that significant improvements can be achieved with the proposed approach.
Keywords
machine learning,deep learning,neural networks,artificial intelligence,data science
How to Cite
Johnson, M., Yılmaz, A., Müller, H., Tanaka, Y., & Petrova, E. (2020). Serverless Computing Architectures for Web Applications. Nivo Light - International Journal of Research & Innovation, 2(1), 58–63. https://doi.org/10.28051/ojstest-40
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