Blockchain-Enabled Governance for Cloud Platforms with Real-Time APIs and Zero-Downtime AI and Machine Learning Migration
Abstract
Modern cloud platforms increasingly host mission-critical artificial intelligence (AI) and machine learning (ML) workloads that demand high availability, governance transparency, and secure interoperability. As enterprises adopt microservices and real-time APIs, maintaining zero-downtime migration of AI/ML models across distributed cloud environments becomes both a technical and governance challenge. This paper proposes a blockchain-enabled governance framework for cloud platforms integrating real-time API management and seamless AI/ML migration strategies. The architecture leverages permissioned blockchain networks to provide immutable audit trails, policy enforcement, and decentralized trust across multi-cloud infrastructures. Real-time APIs ensure low-latency data exchange, while CI/CD-driven MLOps pipelines support continuous model integration, validation, and deployment without service interruption. Techniques such as blue-green deployment, canary releases, container orchestration, and federated model registries enable zero-downtime transitions. The proposed framework enhances accountability, compliance, traceability, and operational resilience. Performance evaluation considers latency, scalability, fault tolerance, and governance efficiency. Findings indicate that integrating blockchain governance with real-time APIs and automated ML migration pipelines significantly improves transparency, security, and reliability in cloud-based AI ecosystems. The study contributes a unified methodology for secure, compliant, and uninterrupted AI/ML lifecycle management in distributed cloud environments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14881-14888 |
Published |
September 9, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Daniel Bruns (2024). Blockchain-Enabled Governance for Cloud Platforms with Real-Time APIs and Zero-Downtime AI and Machine Learning Migration. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14881-14888. https://doi.org/10.15662/IJAESIT.2024.0705004 |
References
2. Maria Kabtia, M. K., Jannatul Ferdousi, J. F., Md Ashraful Alam, M. A. A., & Md Majedul Hasan, M. M. H. (2023). Impact of AI Personalization Algorithms on Customer Trust and Data Privacy Compliance in the United States. Impact of AI Personalization Algorithms on Customer Trust and Data Privacy Compliance in the United States, 6(12), 163-188.
3. Selvi, C. P., Muneeshwari, P., Selvasheela, K., & Prasanna, D. (2023). Twitter media sentiment analysis to convert non-informative to informative using QER. Intelligent Automation & Soft Computing, 35(3).
4. Muthirevula, G. R., Amarapalli, L., & Keezhadath, A. A. (2024). Blockchain for secure data lifecycle management in FDA-regulated environments. Journal of AI-Powered Medical Innovations, 3(1), 137–152.
5. Gangina, P. (2023). Service mesh implementation strategies for zero-downtime migrations in production environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7208–7220.
6. Kumar, R., Mohammed, A. S., & Murthy, C. J. (2023). Cash management forecasting using long short-term memory networks. American Journal of Cognitive Computing and AI Systems, 7, 123–155.
7. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
8. Ponugoti, M. (2023). Frameworks for ensuring compliance in digital platform governance. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7575–7586.
9. Pimpale, Siddhesh. (2021). Power Electronics Challenges and Innovations Driven by Fast- Charging EV Infrastructure. International Journal of Intelligent Systems and Applications in Engineering. 9. 144.
10. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). 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.
11. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342–5351.
12. Chennamsetty, C. S. (2024). Real-time notifications and event-driven architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9686–9691.
13. Kamadi, S. (2022). Adaptive federated data science and MLOps architecture. International Journal of Scientific Research in Computer Science Engineering and Information Technology (IJSRCSEIT), 8(6), 745–755.
14. Vijayaboopathy, V., Rao, S. B. S., & Surampudi, Y. (2023). Strategic modernization of regional health plan data platforms. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 172–208.
15. Ramidi, M. (2023). Accessibility-centered mobile architectures for government health initiatives. International Journal of Research and Applied Innovations (IJRAI), 6(2), 8597–8610.
16. 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: Proceedings of IEMIS 2020 (Vol. 3, p. 155).
17. Surisetty, L. S. (2023). Proactive threat mitigation in API ecosystems through AI-powered anomaly detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633–7642.
18. Gaddapuri, N. S. (2023). A comparative study of healthcare systems in the United States and India. Power System Protection and Control, 51(2), 18–31.
19. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
20. Namdeo, A. (2022). Federated learning BI across multi-cloud data silos. The International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7893–7903.
21. Parasa, M. (2021). TEAL-HCM: A tamper-evident AI lineage framework for securing cloud-based SAP Success Factors integrations. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 13(2), 180–194. https://doi.org/10.18090/samriddhi.v13i02.18
22. Joyce, S. (2023). Optimizing SAP workloads on cloud-native platforms: A framework for intelligent resource allocation and performance scaling. International Journal of Science, Research and Technology (IJSRAT), 6(1), 9210–9219. https://doi.org/10.15662/IJSRAT.2023.0601002
23. Subramanyam, S. P. (2023). Cloud infrastructure automation and role-based access governance in Azure Kubernetes services. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8392–8400.
24. Panyala, V. R., & Pappu, H. (2021). Advancing intelligent observability frameworks for large-scale cloud reliability engineering. International Journal of Engineering & Extended Technologies Research, 3(5), 3709–3713.
25. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Computer Technology and Electronics Communication, 6(4), 7352–7356
26. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.
27. Narayanan, S. (2022). Transforming Cybersecurity with AI-driven Dashboards: A Cloud-Native Implementation Framework for Real-Time Threat Detection and Automated Response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.
28. V. B. Sarabu. (2018). A framework-driven approach to data validation and reconciliation for operational accuracy. International Journal of Research and Applied Innovations, 1(1), 2130–2140.
29. Hossain, M. S., Rahman, M. W., Hossain, M. S., & Ali, M. (2023). Applying Predictive Analytics to Optimize Government Operations and Improve Public Service Delivery in the United States. Applying Predictive Analytics to Optimize Government Operations and Improve Public Service Delivery in the United States, 1(8), 170-196.
30. Prasad, P. K. (2019). DevSecOps: Securing infrastructure in the age of automation. International Journal of Research Publication in Engineering, Technology and Management, 2(1), 930–938.
31. Subramani, V. (2022). Architectural Approaches for Securing Cloud Native Microservices. International Journal of Computer Technology and Electronics Communication, 5(3), 5169-5176.
32. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using artificial intelligence based natural language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735–1739). IEEE.
33. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7691–7702. https://doi.org/10.15662/IJRAI.2022.0505007
34. Chittoor, P. K., Chokkalingam, B., Verma, R., & Mihet-Popa, L. (2023). An assessment of shortest prioritized path-based bidirectional wireless charging approach toward smart agriculture. IEEE Access, 11, 123742–123755.
35. Ananth, S., Balaji, N. G., Prasad, P., Bhargavi, L. N., & Iyyanar, D. (2023). Design and implementation of smart guided glass for visually impaired people. International Journal of Electrical and Computer Engineering, 5(11), 1691–1704.
36. Zerine, I., Islam, M. M., Islam, M. S., Ahmad, M. Y., & Rahman, M. A. (2020). Climate risk analytics for US agriculture sustainability. Cuestiones de Fisioterapia, 49(3), 241–258.
37. Genne, S. (2023). A secure bridge-based execution architecture for hybrid mobile applications. International Journal of Research and Applied Innovations (IJRAI), 6(1), 8316–8328.
38. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the power of machine learning for diabetes risk assessment. In 2023 International Conference on Data Science Agents & Artificial Intelligence (ICDSAAI) (pp. 1–6). IEEE.
39. Sumathi, R., & Umasankar, P. (2023). A hybrid approach for power flow management in smart grid connected system. IETE Journal of Research, 69(8), 5204–5218.