Cloud-Native Intelligent Systems for Predictive Analytics Infrastructure Optimization and Secure Digital Platforms

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Published: 2024-09-16
DOI: https://doi.org/10.15662/IJRPETM.2024.0705012
Keywords:
Cloud-native computing, Predictive analytics, Intelligent systems, Infrastructure optimization, Cybersecurity, Microservices architecture, Machine learning, Digital platforms

Main Article Content

Poornima G
Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai, India

Abstract

Cloud-native intelligent systems are transforming modern digital infrastructure by enabling scalable, adaptive, and secure computing environments. Organizations increasingly rely on predictive analytics and artificial intelligence to optimize cloud resources, improve operational efficiency, and strengthen cybersecurity measures. Cloud-native architectures—built on microservices, containers, orchestration platforms, and serverless computing—provide the foundation for intelligent systems capable of dynamically monitoring and optimizing infrastructure performance. These systems leverage machine learning algorithms to analyze large volumes of operational data, predict potential system failures, optimize workload distribution, and automate infrastructure management.
 

Predictive analytics plays a crucial role in forecasting demand, detecting anomalies, and preventing system downtime, thereby improving reliability and reducing operational costs. Additionally, integrating intelligent security frameworks enhances the protection of digital platforms against evolving cyber threats through real-time threat detection and automated response mechanisms. Cloud-native intelligent systems also facilitate continuous integration and deployment, enabling rapid innovation and flexible scaling across distributed environments.


 


This research explores the architecture, implementation strategies, and benefits of cloud-native intelligent systems for predictive analytics infrastructure optimization and secure digital platforms. The study reviews existing technologies, analyzes relevant frameworks, and proposes a methodology for implementing intelligent cloud infrastructures. The findings demonstrate that cloud-native intelligent systems significantly enhance system performance, resource efficiency, and security in modern digital ecosystems.

Article Details

Issue

Vol. 7 No. 5 (2024): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Cloud-Native Intelligent Systems for Predictive Analytics Infrastructure Optimization and Secure Digital Platforms. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11221-11232. https://doi.org/10.15662/IJRPETM.2024.0705012

References

1. Luo, M., & Zhang, L.-J. (2023). Advances in cloud computing architectures and AI-enabled services. In Cloud computing – CLOUD 2023. Springer.

2. Bhatnagar, G., Rajoria, Y. K., Sakeel, M., Vigenesh, M., Premananthan, G., & Dongre, D. (2023). IoT malware detection tool with CNN classification for small devices. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 2017–2023). IEEE.

3. Mangukiya, M. (2023). Blockchain-enabled traceability and compliance in global electronics production networks. International Journal of Computer Technology and Electronics Communication, 6(6), 7999–8004.

4. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research, 4(5), 5342–5351.

5. Balaji, K. V., & Sugumar, R. (2023). Harnessing the power of machine learning for diabetes risk assessment: A promising approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1–6). IEEE.

6. Sivanantham, E., Vijayakumar, R., Veda, P., Nithya, A., Vinayagam, P. V., & Renukadevi, S. (2024). Optimizing smart methane farms: Intelligent waste sorting for maximum biogas yield through Naive Bayes and IoT integration. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1205–1210). IEEE.

7. Karnam, A. (2024). Next-gen observability for SAP: How Azure Monitor enables predictive and autonomous operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006

8. Sarraf, G. (2023). Autonomous ransomware forensics: Advanced ML techniques for attack attribution and recovery. International Journal of Advanced Research in Science, Communication and Technology, 3(3), 1377–1390. https://doi.org/10.48175/IJARSCT-11978W

9. Madathala, H., Barmavat, B., & Thumala, S. (2023). Performance optimization of SAP HANA using AI-based workload predictions. International Journal of Innovative Research in Science, Engineering and Technology, 12, 15315–15326.

10. Panda, S. S. (2023). Agile quality in the cloud leading Azure RDOS testing and release management. International Journal of Humanities and Information Technology, 5(2), 19–25.

11. Meka, S. (2022). Engineering insurance portals of the future: Modernizing core systems for performance and scalability. International Journal of Computer Science and Information Technology Research, 3(1), 180–198.

12. Vootla, A. (2023). Continuous accessibility assurance through DevSecOps-integrated testing pipelines. International Journal of Research and Applied Innovations, 6(6), 9975–9984.

13. Poornima, G., & Anand, L. (2024). Effective machine learning methods for the detection of pulmonary carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1–7). IEEE.

14. Paul, D., Namperumal, G., & Surampudi, Y. (2023). Optimizing LLM training for financial services: Best practices for model accuracy, risk management, and compliance in AI-powered financial applications. Journal of Artificial Intelligence Research and Applications, 3(2), 550–588.

15. Potel, R. (2022). AI-driven security graphs for real-time breach containment in hybrid cloud environments. International Journal of AI, Big Data, Computational and Management Studies, 3(4), 123–131.

16. Konda, S. K. (2024). Carbon-native DCIM architectures for AI data centers: Autonomous infrastructure control via smart grid intelligence. World Journal of Advanced Research and Reviews, 21(1), 3008–3318. https://doi.org/10.30574/wjarr.2024.21.1.0095

17. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-learning scheduler for multi-tenant Spark clusters under privacy constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496–527.

18. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.

19. Gopinathan, V. R. (2024). AI-driven customer support automation: A hybrid human–machine collaboration model for real-time service delivery. International Journal of Technology, Management and Humanities, 10(1), 67–83.

20. Karvannan, R. (2023). Real-time prescription management system intake & billing system. International Journal of Humanities and Information Technology, 5(2), 34–43.

21. Dave, B. L. (2023). Enhancing vendor collaboration via an online automated application platform. International Journal of Humanities and Information Technology, 5(2), 44–52.

22. Kothokatta, L. (2023). AI-augmented quality engineering for MLOps: Intelligent test orchestration and model reliability on AWS. International Journal of Computer Technology and Electronics Communication, 6(4), 7324–7330.

23. Dama, H. B. (2023). Designing highly available multi-cloud database architectures for global financial services. International Journal of Research and Applied Innovations, 6(1), 8329–8336.

24. Ramsugeerthi, A., Neela Madheswari, A., Umamaheswari, A., & Prassana, D. (2020). Location navigation assistance for educational institutions using augmented reality. Journal of Xidian University, 14(4), 1342–1347. https://doi.org/10.37896/jxu14.4/156

25. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022). Automation using artificial intelligence based natural language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735–1739). IEEE.

26. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943–948). IEEE.

27. Sanepalli, U. R. (2024). GitOps security architecture with zero trust: Identity-driven control planes for cloud-native deployments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(2), 1198–1209. https://doi.org/10.32628/CSEIT24102255

28. Karvannan, R. (2023). Real-time prescription management system intake & billing system. International Journal of Humanities and Information Technology, 5(2), 34–43.

29. Gurumoorthy, T. (n.d.). Neuro fuzzy sliding mode control technique for voltage tracking in boost converter.

30. Suganthi, M., & Ramesh, N. (2022). Treatment of water using natural zeolite as membrane filter. Journal of Environmental Protection and Ecology, 23(2), 520–530.

31. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661

32. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003

33. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.

34. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

35. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.

36. Rengarajan, A., & Rajagopalan, S. (2021). Chaos blend LFSR-duo approach on FPGA for medical image security. In Emerging Technologies in Data Mining and Information Security (pp. 155–162).

37. Ireddy, R. K. (2024). Real-time payment orchestration and fraud governance framework: Cloud-native treasury optimization with ensemble deep learning integration. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(3), 1152–1161. https://doi.org/10.32628/CSEIT25113583

38. Luo, M., & Zhang, L.-J. (Eds.). (2023). Cloud computing – CLOUD 2023: 16th International Conference, SCF 2023 proceedings. Springer. https://doi.org/10.1007/978-3-031-51709-9