AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications

Authors

  • Munivel Devan Compunnel Inc, USA Author
  • Lavanya Shanmugam Tata Consultancy Services, USA Author
  • Manish Tomar Citibank, USA Author

Keywords:

Cloud migration

Abstract

The exponential growth of data generation and storage necessitates efficient and secure data migration strategies for organizations transitioning to cloud environments. Traditional data migration processes are often manual, labor-intensive, and prone to errors, hindering seamless cloud adoption. This research paper delves into the transformative potential of Artificial Intelligence (AI) in streamlining and optimizing data migration for cloud platforms.

We begin by establishing the landscape of cloud migration challenges, highlighting the complexities associated with data discovery, dependency mapping, transformation, and integration. Subsequently, we explore how AI, specifically Machine Learning (ML) algorithms, can automate and expedite these critical stages. AI-powered data discovery tools leverage natural language processing (NLP) to analyze application code and data schemas, automatically identifying and classifying data assets. This eliminates the need for manual inventorying, saving significant time and resources.

Data dependency mapping, crucial for ensuring data integrity during migration, can be significantly enhanced by AI. Supervised learning algorithms can analyze historical data access patterns to automatically identify dependencies between data sources and applications. This eliminates the error-prone process of manual dependency mapping, fostering a more robust and reliable migration strategy.

Data transformation, a critical step in adapting data formats and structures for cloud compatibility, presents another opportunity for AI intervention. Unsupervised learning techniques can be employed to identify patterns and inconsistencies within data sets. These insights can then be used to train AI models to automate data cleansing, normalization, and transformation tasks. This not only reduces migration time but also improves data quality and consistency within the cloud environment.

The paper further explores the concept of cloud-native architecture and its role in facilitating AI-powered data migration. By embracing serverless computing paradigms, organizations can leverage pre-configured, scalable cloud resources for data migration tasks. This eliminates the need for infrastructure provisioning and management, further streamlining the migration process.

To solidify the theoretical framework, the paper presents real-world applications of AI-powered data migration strategies across diverse industries. We showcase how leading organizations in finance, healthcare, and retail have leveraged AI to achieve faster, more efficient cloud migrations. These case studies demonstrate the tangible benefits of AI, including reduced migration costs, improved data quality, and accelerated time-to-market for cloud initiatives.

The paper concludes by outlining the future directions for AI-powered data migration. We discuss the potential of reinforcement learning algorithms to further automate and optimize migration strategies. Additionally, the integration of AI with DevOps practices holds promise for continuous improvement and monitoring of cloud data pipelines. Finally, we address potential challenges associated with AI implementation, including security considerations and the need for robust training data sets.

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References

Abedini, M., & Cho, S. (2020, December). A Survey of Cloud Migration Research: Results and Open Issues. In 2020 International Conference on Information Networking (ICOIN) (pp. 642-647). IEEE

Akrour, R., Ezziyyani, M., & OuZZidane, A. (2019, July). A Machine Learning Approach for Data Migration Planning in Cloud Environment. In 2019 16th International Conference on New Trends in Intelligent Systems (Natis) (pp. 202-207). IEEE

Al-Rubaie, A., & Khan, S. U. (2021, July). A Hybrid Approach for Cloud Data Migration Planning and Cost Estimation. In 2021 IEEE International Conference on Cloud Engineering (ICEC) (pp. 147-156). IEEE

Chen, M., Mao, Z., Li, Z., & Jin, H. (2019, December). Data Migration as a Service: A Survey. In 2019 IEEE International Conference on Services Computing (SERVICES) (Vol. 2, pp. 169-178). IEEE

Chen, Y., Gong, C., Li, J., Liu, Y., & Liu, Z. (2021, June). A Scalable and Cost-Effective Framework for Serverless Data Migration. In 2021 IEEE International Conference on Cloud Computing (CLOUD) (pp. 213-224). IEEE

Dabbagh, M., Hammoudeh, M., & Jararweh, M. A. (2020, December). A Cloud-Based Framework for Data Migration and Transformation Using Machine Learning. In 2020 International Conference on Information Networking (ICOIN) (pp. 636-641). IEEE

De La Torre, L., Gomez-Miranda, I., & Lopez-Santana, M. (2018, July). Cloud Data Migration: Planning and Performance. In 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 215-222). IEEE

Demirtas, I., & Hassan, A. E. (2021, June). A Scalable and Secure Framework for Big Data Migration to the Cloud. In 2021 IEEE International Conference on Cloud Computing (CLOUD) (pp. 71-82). IEEE

Fang, P., Xiao, Z., & Zhou, S. (2019, December). A Survey on Enterprise Data Migration to Cloud. In 2019 IEEE International Conference on Services Computing (SERVICES) (Vol. 2, pp. 578-587). IEEE

Guo, Z., Liu, S., Wang, Z., Sun, Y., & Yang, L. (2021, June). Serverless Data Migration for Cloud-Native Applications. In 2021 IEEE International Conference on Cloud Computing (CLOUD) (pp. 83-94). IEEE

Armbrust, M., Fox, A., Griffith, R., & Patterson, D. A. (2010). Above the clouds: A Berkeley view of cloud computing. ACM Transactions on Computer Systems (TOCS), 28(1), 1-4.

Dean, J., & Ghemawat, S. (2008, December). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer.

Mao, M., & Liu, Y. (2016). Review of research on cloud data migration. Journal of Computer and Communications, 4(2), 84-90.

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (Special Publication 800-145). National Institute of Standards and Technology.

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Published

27-11-2021

How to Cite

[1]
Munivel Devan, Lavanya Shanmugam, and Manish Tomar, “AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications”, Australian Journal of Machine Learning Research & Applications, vol. 1, no. 2, pp. 79–111, Nov. 2021, Accessed: Nov. 21, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/80

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