Artificial Intelligence Driven Innovations in ModularCloud Infrastructures for Optimized ResourceManagement

Authors

  • Fawn Parker McKay CANADA Author

Keywords:

Artificial Intelligence, Modular Cloud Systems, Resource Management, Predictive Scaling, Optimization, Fault Tolerance

Abstract

The adoption of Artificial Intelligence (AI) in modular cloud infrastructures has transformed resource management, enabling predictive scaling, cost optimization, and improved fault tolerance. This paper explores AI-driven innovations in modular cloud systems, reviews literature and proposes a framework for efficient resource allocation. It discusses key challenges, applications, and case studies, and concludes with future research directions.

References

Smith, John, and Emily Harris. "AI in Modular Cloud Systems." Journal of Cloud Computing, vol. 14, no. 3, 2020, pp. 123–140.

Dengshun A Wang. (2021). Comparative Analysis of Serverless Computing and Containerized Deployment in Cloud Platforms. International Journal of Advanced Research in Cloud Computing, 2(2), 1-5.

Patel, Ravi, and Sunil Kumar. "Predictive Scaling with AI." Journal of Distributed Systems, vol. 12, no. 4, 2019, pp. 201–218.

Sheta, S. V. (2023). The role of test-driven development in enhancing software reliability and maintainability. Journal of Software Engineering (JSE), 1(1), 13–21.

Nivedhaa, N. (2024). Software architecture evolution: Patterns, trends, and best practices. International Journal of Computer Sciences and Engineering (IJCSE), 1(2), 1-14.

Lee, Mark, and Kevin Davis. "Fault Tolerance in AI-Driven Cloud Systems." Journal of Artificial Intelligence Applications, vol. 10, no. 2, 2018, pp. 67–84.

Vinay, S. B. (2024). Automated data transformation processes for improved efficiency and accuracy in complex ETL workflows. International Journal of Data Engineering Research and Development (IJDERD), 1(2), 1–11.

Sheta, S. V. (2023). Developing efficient server monitoring systems using AI for real-time data processing. International Journal of Engineering and Technology Research (IJETR), 8(1), 26–37.

Chen, Wei, and Kevin Hall. "AI Optimization in Modular Architectures." Journal of Advanced Analytics, vol. 9, no. 4, 2019, pp. 87–105.

Lopez, Carlos, and Megan Hughes. "Efficiency in AI Resource Management." Journal of Cloud Technologies, vol. 8, no. 3, 2020, pp. 245–261.

Sheta, S. V. (2024). Challenges and solutions in troubleshooting database systems for modern enterprises. International Journal of Advanced Research in Engineering and Technology (IJARET), 15(1), 53–66.

Vinay, S. B. (2024). A comprehensive analysis of big data-driven innovations in precision medicine and genomics. International Journal of Big Data Intelligence (IJBDI), 1(1), 1–10.

Rivera, Carlos, and Sarah White. "Energy-Efficient Modular AI Systems." Journal of Cloud Data Engineering, vol. 12, no. 5, 2020, pp. 201–218.

Sheta, S. V. (2024). Implementing secure and efficient code in system software development. International Journal of Information Technology and Management Information Systems (IJITMIS), 15(2), 34–46.

Gupta, A. (2024). Economic forecasting with multi-modal financial data integration. QIT Press - International Journal of Financial Data Science Research, 5(2), 1–5. Published August 6, 2024.

Anderson, Michael, and Laura Peters. "Dynamic AI-Driven Resource Management in Modular Cloud Systems." Journal of Cloud Optimization, vol. 13, no. 4, 2020, pp. 211–230.

Jain, A. V. (2023). Developing advanced threat intelligence systems for proactive cybersecurity defense mechanisms. International Journal of Advanced Research in Cyber Security, 4(2), 1–5.

Sheta, S. V. (2024). The role of adaptive communication skills in IT project management. Journal of Computer Engineering and Technology (JCET), 7(2), 27–39.

Brown, David, and Megan Lewis. "Leveraging Machine Learning for Predictive Scaling in Cloud Infrastructures." Journal of Artificial Intelligence and Applications, vol. 11, no. 2, 2019, pp. 145–162.

Hannah Jacob. (2023). Exploring Blockchain and Data Science for Next-Generation Data Security. International Journal of Computer Science and Information Technology Research , 4(2), 1-9.

Chen, Li, and Wei Zhang. "Interoperability Challenges in AI-Driven Cloud Architectures." International Journal of Advanced Computing, vol. 12, no. 3, 2020, pp. 89–103.

Garcia, Maria, and Robert Hall. "AI-Powered Optimization in Modular Systems." Journal of Emerging Cloud Computing Trends, vol. 14, no. 1, 2020, pp. 67–84.

Christopher Henry Brighton. (2023). The Role of AI 2.0 in Transforming Business Processes. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 13(2), 60-68.

Johnson, Emily, and Kevin Brown. "Reducing Latency in Modular AI Workflows." Journal of Distributed Computing Applications, vol. 15, no. 3, 2021, pp. 145–162.

Kumar, P. T. (2023). A quantitative study of privacy-preserving techniques in federated learning for distributed systems. International Journal of Artificial Intelligence, 4(1), 1–4.

Rivera, Carlos, and Sarah White. "Energy Efficiency in AI-Driven Resource Management." Journal of Cloud Data Engineering, vol. 12, no. 5, 2020, pp. 201–218.

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Published

2024-07-17

How to Cite

Fawn Parker McKay. (2024). Artificial Intelligence Driven Innovations in ModularCloud Infrastructures for Optimized ResourceManagement. INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY RESEARCH & DEVELOPMENT, 5(2), 6-11. https://ijetrd.com/index.php/ijetrd/article/view/IJETRD_5_2_002