ISSN: 0000-0000 e-ISSN: 0000-0000
Open Access

Infrastructure as Code Best Practices

1 University of Bologna, Italy
2 University of Melbourne, Australia
3 INRAE, France
4 National and Kapodistrian University of Athens, Greece
5 Bilkent University, Turkey

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 33% 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

How to Cite

Rossi, M., Wilson, E., Martin, P., Papadopoulos, A., & Şahin, B. (2022). Infrastructure as Code Best Practices. Nivo Light - International Journal of Research & Innovation, 4(4), 27–34. https://doi.org/10.28051/ojstest-145

References

📄 Smith, J., & Brown, A. (2018). Deep learning applications in urban computing. Journal of Smart Cities, 15(3), 234-251.
📄 Wang, L., Chen, H., & Liu, X. (2017). Energy optimization algorithms for intelligent buildings. Energy and Buildings, 142, 45-58.
📄 García, M., Rodriguez, P., & Martinez, S. (2018). Sustainable urban development: A comprehensive review. Sustainability Science, 13(2), 189-205.
📄 Tanaka, K., & Yamamoto, H. (2016). Smart grid technologies and renewable energy integration. IEEE Transactions on Smart Grid, 7(4), 1892-1901.
📄 Anderson, R., Williams, T., & Davis, M. (2017). Machine learning for demand response systems. Applied Energy, 201, 112-125.

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