AI-Driven ERP Optimization: Cloud-Native DevOps and Markov Decision Processes for Scalable Web Application Ecosystems
DOI:
https://doi.org/10.15680/IJCTECE.2024.0701006Keywords:
Cloud Migration, ERP Systems, DevOps, Automation, Oracle Database Management, Scalability, Data Security, Digital Privacy, Enterprise Applications, Continuous IntegrationAbstract
The integration of scalable Enterprise Resource Planning (ERP) systems with cloud-native DevOps strategies is pivotal for modern enterprises aiming to enhance operational efficiency and agility. This research evaluates online automated applications facilitating the migration of ERP systems to cloud environments, focusing on Oracle Database Management. The study examines frameworks that automate the migration process, ensuring minimal disruption and optimized performance. By leveraging cloud-native DevOps practices, organizations can achieve continuous integration and delivery, fostering a culture of collaboration and rapid iteration. The research methodology includes a systematic review of existing literature, case studies, and empirical data to assess the effectiveness of these automated frameworks. Key findings highlight the benefits of automation in reducing manual intervention, enhancing scalability, and improving data security during migration. However, challenges such as data privacy concerns, integration complexities, and the need for skilled personnel are also identified. The study concludes with recommendations for organizations considering ERP migration, emphasizing the importance of selecting appropriate frameworks and adopting best practices to mitigate risks and maximize benefits.
References
1. Paraiso, F., Merle, P., & Seinturier, L. (2014). soCloud: A service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds. arXiv. Retrieved from https://arxiv.org/abs/1407.1963
2. DrR. Udayakumar, Muhammad Abul Kalam (2023). Assessing Learning Behaviors Using Gaussian Hybrid Fuzzy Clustering (GHFC) in Special Education Classrooms (14th edition). Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (Jowua) 14 (1):118-125.
3. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
4. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explain ability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1-7.
5. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
6. Reddit. (2022). Automate Oracle Integration Export Import Process | OIC. Retrieved from https://www.reddit.com/r/u_TechSupper/comments/wy27ai
7. Shewale, V. (2022). IT/OT Convergence: A Zero Trust Reference Architecture for the Energy Sector. International Journal of Science, Research and Technology, 5(5), 8494-8502.
8. Salih, S., Hamdan, M., Abdelmaboud, A., Abdelaziz, A., Abdelsalam, S., Althobaiti, M. M., Cheikhrouhou, O., Hamam, H., & Alotaibi, F. (2021). Prioritising organisational factors impacting cloud ERP adoption and the critical issues related to security, usability, and vendors: A systematic literature review. Sensors, 21(24), Article 8391. https://doi.org/10.3390/s21248391 SpringerOpen+1
9. Kiran Nittur, Srinivas Chippagiri, Mikhail Zhidko, “Evolving Web Application Development Frameworks: A Survey of Ruby on Rails, Python, and Cloud-Based Architectures”, International Journal of New Media Studies (IJNMS), 7 (1), 28-34, 2020.
10. Waseem, M., Liang, P., & Shahin, M. (2020). A systematic mapping study on microservices architecture in DevOps. arXiv. https://arxiv.org/abs/2008.07729 arXiv
11. Taibi, D., Lenarduzzi, V., & Pahl, C. (2019). Continuous architecting with microservices and DevOps: A systematic mapping study. arXiv. https://arxiv.org/abs/1908.10337 arXiv
12. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices. arXiv. (preprint). arXiv
13. Buyya, R., Calheiros, R. N., & Li, X. (2012). Autonomic Cloud Computing: Open Challenges and Architectural Elements. arXiv. https://arxiv.org/abs/1209.3356 arXiv
14. Hannousse, A., & Yahiouche, S. (2020). Securing microservices and microservice architectures: A systematic mapping study. arXiv. https://arxiv.org/abs/2003.07262 arXiv
15. Gosangi, S. R. (2023). Reimagining Government Financial Systems: A Scalable ERP Upgrade Strategy for Modern Public Sector Needs. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8001-8005.
16. 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.
17. 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.
18. 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
19. 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
20. 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.
21. 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.
22. 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
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. Subramani, V. (2022). Architectural Approaches for Securing Cloud Native Microservices. International Journal of Computer Technology and Electronics Communication, 5(3), 5169-5176.
29. Venkata Ramana Reddy Bussu,, Sankar, Thambireddy, & Balamuralikrishnan Anbalagan. (2023). EVALUATING THE FINANCIAL VALUE OF RISE WITH SAP: TCO OPTIMIZATION AND ROI REALIZATION IN CLOUD ERP MIGRATION. International Journal of Engineering Technology Research & Management (IJETRM), 07(12), 446–457. https://doi.org/10.5281/zenodo.15725423
30. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
31. Sugumar R., et.al IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES, Revista de Gestao Social e Ambiental, V-17, I-4, 2023.
32. Shewale, V. (2023). Operationalizing NIST CSF 2.0 and TSA Security Directives in Pipeline Cybersecurity. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9773-9779.
33. AZMI, S. K. (2021). Markov Decision Processes with Formal Verification: Mathematical Guarantees for Safe Reinforcement Learning.
34. Jaafar Mohammed, G., & Burhanuddin, M. A. (2018). Cloud‐Based ERP Implementation in SMEs: A literature survey. International Journal of Engineering and Technology, 7(3.20), 753 755. https://doi.org/10.14419/ijet.v7i3.20.26743 sciencepubco.com
