AI-Augmented Cloud Integration: Future-Proofing Migration and Middleware

Soujanya Vummannagari ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 11, 38 - 52

Abstract: Enterprise computing environments undergo fundamental transformation as organizations transition from traditional monolithic systems toward distributed, cloud-native infrastructures. Artificial intelligence serves as the primary catalyst driving revolutionary changes in migration and integration methodologies. Machine learning algorithms enable predictive assessment capabilities that evaluate system preparedness, map complex dependencies, and anticipate operational bottlenecks before deployment phases begin. Automated refactoring technologies transform legacy code bases through advanced semantic analysis, identifying optimal microservice boundaries while maintaining essential business logic relationships. Continuous integration and deployment pipelines reach unprecedented efficiency levels through reinforcement learning mechanisms that dynamically allocate resources and optimize testing protocols without compromising quality standards. Complex schema reconciliation processes benefit from adaptive transformation engines that automatically adjust to structural changes while preserving data integrity across diverse integration points. Advanced monitoring frameworks establish intelligent baselines and predict system failures before end-user experiences degradation. Explainable artificial intelligence ensures transparency and maintains governance standards as middleware operations become increasingly autonomous. Combined innovations transform static integration components into intelligent, self-adapting architectural foundations designed for modern enterprise computing requirements.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.carijournals.org/journals/index.php/IJCE/article/view/2970 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bhx:ojijce:v:7:y:2025:i:11:p:38-52:id:2970

Access Statistics for this article

More articles in International Journal of Computing and Engineering from CARI Journals Limited
Bibliographic data for series maintained by Chief Editor ().