Dr. Vishnu Menon
Independent Researcher
Kerala, India
Abstract
The migration of data to cloud environments is a critical process for organizations seeking enhanced performance, scalability, and cost-efficiency. However, the complexity of data integration and migration poses significant challenges, including data loss, security issues, and extended downtime. This paper explores the application of machine learning (ML) techniques to streamline cloud migrations and optimize data integration processes. By leveraging ML algorithms for predictive analytics, anomaly detection, and automation, organizations can improve the reliability and efficiency of their migration strategies. This study presents a framework for implementing ML-driven solutions, demonstrating their effectiveness through case studies. The findings indicate that organizations adopting machine learning can achieve seamless cloud migrations while ensuring data integrity and accessibility.
Keywords
Cloud Migration, Data Integration, Machine Learning, Predictive Analytics, Anomaly Detection, Automation, Data Management.
References
- Kirchoff, D. F., Xavier, M. G., Mastella, J., & de Rose, C. A. F. (2019). A preliminary study of machine learning workload prediction techniques for cloud applications. In Proceedings of the 2019 Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp. 1–6). IEEE. https://doi.org/10.1109/EMPDP.2019.8671604 researchgate.net
- Sudhakar Tiwari. (2023). Biometric Authentication in the Face of Spoofing Threats: Detection and Defense Innovations. Innovative Research Thoughts, 9(5), 402–420. https://doi.org/10.36676/irt.v9.i5.1583
- Manias, D. M., Hawilo, H., & Shami, A. (2020). A machine learning-based migration strategy for virtual network function instances. In Proceedings of the 2020 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC45622.2020.9149047 arxiv.org
- Wang, Z., Zhou, L., Das, A., Dave, V., Jin, Z., & Zou, J. (2020). Survive the schema changes: Integration of unmanaged data using deep learning. In 2020 IEEE International Conference on Big Data (BigData) (pp. 4837–4847). IEEE. https://doi.org/10.1109/BigData50022.2020.9378299 arxiv.org
- Hai, R., Koutras, C., Ionescu, A., Li, Z., Sun, W., van Schijndel, J., Kang, Y., & Katsifodimos, A. (2022). Amalur: Data integration meets machine learning. Proceedings of the VLDB Endowment, 14(12), 2818–2821. https://doi.org/10.14778/3548555.3548575 arxiv.org
- Xu, M., Song, C., Wu, H., Gill, S. S., Ye, K., & Xu, C. (2022). EsDNN: Deep neural network based multivariate workload prediction approach in cloud environment. IEEE Transactions on Cloud Computing, 10(3), 1335–1347. https://doi.org/10.1109/TCC.2022.3183746 arxiv.org
- Cahoon, J., Wang, W., Zhu, Y., Lin, K., Liu, S., Truong, R., Singh, N., Wan, C., Ciortea, A. M., Narasimhan, S., & Krishnan, S. (2022). Doppler: Automated SKU recommendation in migrating SQL workloads to the cloud. Proceedings of the VLDB Endowment, 15(12), 3532–3544. https://doi.org/10.14778/3557828.3558294 arxiv.org
- Rossi, A., Visentin, A., Carraro, D., Prestwich, S., & Brown, K. N. (2023). Forecasting workload in cloud computing: Towards uncertainty-aware predictions and transfer learning. arXiv preprint arXiv:2303.13525. https://doi.org/10.48550/arXiv.2303.13525 arxiv.org
- Setayesh, A., Hadian, H., & Prodan, R. (2023). An efficient online prediction of host workloads using pruned GRU neural nets. arXiv preprint arXiv:2303.16601. https://doi.org/10.48550/arXiv.2303.16601 arxiv.org
- Zhang, Q., & Xu, X. (2018). Workload prediction in cloud using artificial neural network and self-adaptive differential evolution. Future Generation Computer Systems, 82, 218–234. https://doi.org/10.1016/j.future.2017.06.022 sciencedirect.com
- Haase, C., Röseler, T., & Seidel, M. (2022). METL: A modern ETL pipeline with a dynamic mapping matrix. arXiv preprint arXiv:2203.10289. https://doi.org/10.48550/arXiv.2203.10289 arxiv.org
