The role of AI and Machine learning in Optimizing Cloud Migration Processes

Volume: 10    Issue: 2

Year of Publication: 2025

Pages: (1-7)

Authors: Govindaiah Simuni, Amarantha Atla

DOI: https://doi.org/10.5281/zenodo.15574402

Download Full Text (PDF)

Abstract

The systematic literature review explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud migration processes. It highlights that these technologies improve efficiency by automating workload assessments, optimizing resource allocation, and enabling predictive analytics for risk management. Key findings indicate that tools like AWS Migration Hub and Azure Migrate significantly streamline migrations, reducing costs and time. However, challenges like a shortage of qualified personnel and data security concerns persist. The review concludes that adopting AI and ML can lead to more effective cloud migration strategies, with future directions focusing on integrating emerging technologies and exploring user-centric approaches.

References

1. Bukhari, O., Agarwal, P., Koundal, D., & Zafar, S. (2023). Anomaly detection using ensemble techniques for boosting the security of intrusion detection system. Procedia Computer Science, 218, 1003-1013. https://doi.org/10.1016/j.procs.2023.01.080

2. Kumar, K. P., Prathap, B. R., Thiruthuvanathan, M. M., Murthy, H., & Pillai, V. J. (2023). Secure approach to sharing digitized medical data in a cloud environment. Data Science and Management, 7(2). https://doi.org/10.1016/j.dsm.2023.12.001

3. Cao, R., & Iansiti, M. (2022). Digital transformation, data architecture, and legacy systems. Journal of Digital Economy.https://doi.org/10.1016/j.jdec.2022.07.001

4. Chang, V., Doan, L. M. T., Xu, Q. A., Hall, K., Wang, Y. A., & Kamal, M. M. (2023). Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices. Journal of Business Research, 156, 113369. https://doi.org/10.1016/j.jbusres.2022.113369

5. Xu, Q. A., Jayne, C., & Chang, V. (2024). An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews. Technological Forecasting and Social Change, 202, 123326-123326. https://doi.org/10.1016/j.techfore.2024.123326

6. Mohsen, B. M. (2023). Developments of Digital Technologies Related to Supply Chain Management. Procedia Computer Science, 220, 788-795. https://doi.org/10.1016/j.procs.2022.12.305

7. Chang, V., Hahm, N., Xu, Q. A., Vijayakumar, P., & Liu, L. (2024). Towards data and analytics-driven B2B-banking for green finance: A cross-selling use case study. Technological forecasting & social change, 206, 123542-123542. https://doi.org/10.1016/j.techfore.2024.123542

8. Dominic, N., Cenggoro, T. W., & Pardamean, B. (2023). Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes. Procedia computer science, 227, 1054-1061. https://doi.org/10.1016/j.procs.2023.10.615

9. Bangui, H., Buhnova, B., & Ge, M. (2023). Social Internet of Things: Ethical AI Principles in Trust Management. Procedia Computer Science, 220, 553-560. https://doi.org/10.1016/j.procs.2023.03.070

10. Maruthu, S. S., & Simuni, G. (2022). Predicting personality traits through self-introductory messages using AI. SSRN. https://ssrn.com/abstract=5053433 or http://dx.doi.org/10.2139/ssrn.5053433

11. Konakalla, P., & Simuni, G. (2024). Security and privacy concerns in generative AI. SSRN. https://ssrn.com/abstract=5052837 or http://dx.doi.org/10.2139/ssrn.5052837

12. Atla, A., & Simuni, G. (2023). The role of AI and machine learning in optimizing cloud migration processes. SSRN. https://ssrn.com/abstract=4982496 or http://dx.doi.org/10.2139/ssrn.4982496

13. Farahzadi, A., Farahsary, P. S., & Rezazadeh, J. (2021). A survey on IoT fog nano datacenters. Wireless Networks, 28(1), 173-207. https://doi.org/10.1007/s11276-021-02829-2

14. Kumar, R., Aljuhani, A., Javeed, D., Kumar, P., Islam, S., & Islam, A. K. M. N. (2024). Digital twins-enabled zero-touch network: A smart contract and explainable AI integrated cybersecurity framework. Future generation computer systems, 156, 191-205. https://doi.org/10.1016/j.future.2024.02.015

15. Mogadem, M. M., Li, Y., & Meheretie, D. L. (2021). A survey on internet of energy security: related fields, challenges, threats, and emerging technologies. Cluster Computing.https://doi.org/10.1007/s10586-021-03423-z

16. Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1). Retrieved from https://link.springer.com/article/10.1007/s10479-020-03620-w

17. Ullah, N., M. N. Nawi, & Ouhame, S. (2021). Recent advancement in VM task allocation system for cloud computing: a review from 2015 to 2021. Artificial Intelligence Review, 55(3), 2529-2573. https://doi.org/10.1007/s10462-021-10071-7

18. Pratsri, S. & Nilsook, P. (2020). Design on Big Data Platform-Based in Higher Education Institute. Higher Education Studies, 10(4), 36-43. Retrieved September 28, 2024, from https://eric.ed.gov/?q=%22Cloud+Migration%22+AND+(%22Artificial+Intelligence%22+OR+%22AI%22)%22(ML+OR+%22Machine+Learning%22)+AND+(%22Efficiency%22+OR+%22Optimization%22)+AND+(%22Tools%22+OR+%22Techniques%22)&ft=on&ff1=dtySince_2020&ff2=pubJournal+Articles&id=EJ1274960

19. Lozano-Blasco, R., Mira-Aladrén, M., & Gil-Lamata, M. (2023). Social Media Influence on Young People and Children: Analysis on Instagram, Twitter, and YouTube. Comunicar: Media Education Research Journal, 31(74), 117-128. Retrieved September 28, 2024, from https://eric.ed.gov/?q=%22Cloud+Migration%22+AND+(%22Artificial+Intelligence%22+OR+%22AI%22)%22(ML+OR+%22Machine+Learning%22)+AND+(%22Efficiency%22+OR+%22Optimization%22)+AND+(%22Tools%22+OR+%22Techniques%22)&ft=on&ff1=dtySince_2020&ff2=pubJournal+Articles&id=EJ1380410

20. Firmansyah, J., Suhandi, A., Setiawan, A., & Permanasari, A. (2022). Evaluation of Virtual Workspace Laboratory: Cloud Communication and Collaborative Work on Project-Based Laboratory. Journal on Efficiency and Responsibility in Education and Science, 15(3), 131-141. Retrieved September 28, 2024, from https://eric.ed.gov/?q=%22Cloud+Migration%22+AND+(%22Artificial+Intelligence%22+OR+%22AI%22)%22(ML+OR+%22Machine+Learning%22)+AND+(%22Efficiency%22+OR+%22Optimization%22)+AND+(%22Tools%22+OR+%22Techniques%22)&ft=on&ff1=dtySince_2020&ff2=pubJournal+Articles&id=EJ1364579

21. Baharuddin, D., Ampera, H., Fibriasari, S., Muhammad, & Hamid, A. (2021). Implementation of Cloud Computing System in Learning System Development in Engineering Education Study Program. International Journal of Education in Mathematics, Science and Technology, 9(4), 728-740. Retrieved September 28, 2024, from https://eric.ed.gov/?q=%22Cloud+Migration%22+AND+(%22Artificial+Intelligence%22+OR+%22AI%22)%22(ML+OR+%22Machine+Learning%22)+AND+(%22Efficiency%22+OR+%22Optimization%22)+AND+(%22Tools%22+OR+%22Techniques%22)&ft=on&ff1=dtySince_2020&ff2=pubJournal+Articles&id=EJ1318829

22. Oliveri, L. M., Chiacchio, F., D’Urso, D., Munnia, A., & Russo, F. (2023). Successful digital transformations enabled by technologies or by open mind? Italian case studies. Procedia Computer Science, 217, 1066-1075. https://doi.org/10.1016/j.procs.2022.12.305

23. Flash. (2024). Benefits of AI in Cloud Migration and Modernization. Amzur. https://amzur.com/blog/ai-driven-cloud- migration-benefits

Keywords

AI, Machine Learning, Cloud Migration, Efficiency, Predictive Analytics