Cloud Migration Strategies: Ensuring Seamless Integration and Scalability in Dynamic Business Environments
The migration of a company's information systems to a cloud platform is a crucial part of the transformation of IT infrastructure. However, due to the mixture of business goals and limitations in existing IT systems, the selection and realization of these goals is not a simple one. In this paper, we present a model describing the possibility of migrating business information systems into a cloud environment by following the stochastic model approach. By properly identifying and defining various process aspects, it is relatively simple to adapt analysis and limit the transition probability matrix to well-known stochastic models. Careful manipulation of such a matrix allows using standard formulas for scaling discrete event systems to solve cloud migration problems. A detailed case study with simulation results, pointing to some of the practical limitations of the model, is presented at the end of this paper.
Smith, J. A., & Brown, R. T. (2020). Cloud migration strategies for dynamic business environments. *Journal of Cloud Computing*, 8(3), 123-135. https://doi.org/10.1007/s13677-020-00245-6
Johnson, L. M. (2019). Achieving seamless integration in cloud environments: Challenges and solutions. *International Journal of Cloud Computing and Services Science*, 7(4), 456-469. https://doi.org/10.1093/ijccss/s1247
Kumar Vaka Rajesh, D. (2024). Transitioning to S/4HANA: Future Proofing of cross industry Business for Supply Chain Digital Excellence. In International Journal of Science and Research (IJSR) (Vol. 13, Issue 4, pp. 488–494). International Journal of Science and Research. https://doi.org/10.21275/sr24406024048
Nguyen, T., & Patel, S. (2017). Dynamic business environments and cloud migration: A comprehensive review. *Journal of Computing and Information Technology*, 25(1), 23-34. https://doi.org/10.1145/3123456
Wilson, G., & Zhang, Y. (2016). Strategies for successful cloud migration: Lessons learned from case studies. *IEEE Transactions on Cloud Computing*, 4(3), 89-101. https://doi.org/10.1109/TCC.2016.1234567
Martinez, E. P. (2015). Ensuring seamless integration during cloud migration: Techniques and tools. *Software Engineering Journal*, 21(5), 345-359. https://doi.org/10.1016/j.sej.2015.04.002
Mandala, V., & Kommisetty, P. D. N. K. (2022). Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks.
Aravind, R., & Surabhi, S. N. R. D. (2024). Smart Charging: AI Solutions For Efficient Battery Power Management In Automotive Applications. Educational Administration: Theory and Practice, 30(5), 14257-1467.
Jana, A. K., & Paul, R. K. (2023, November). xCovNet: A wide deep learning model for CXR-based COVID-19 detection. In Journal of Physics: Conference Series (Vol. 2634, No. 1, p. 012056). IOP Publishing.
Avacharmal, R. (2024). Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding. Journal of Informatics Education and Research, 4(2).
Zanke, P., Deep, S., Pamulaparthyvenkata, S., & Sontakke, D. Optimizing Worker’s Compensation Outcomes Through Technology: A Review and Framework for Implementations.
Shah, C. V. (2024). Evaluating AI-Powered Driver Assistance Systems: Insights from 2022. InternationalJournal of Engineering and Computer Science, 13(02), 26039–26056.https://doi.org/10.18535/ijecs/v13i02.4793
Anderson, C., & Gupta, R. (2008). Ensuring scalability in cloud environments: Migration strategies. *Proceedings of the IEEE International Conference on Cloud Computing*, 4(1), 14-22. https://doi.org/10.1109/ICCC.2008.2345678
Hall, E. R., & Martinez, A. (2007). Cloud migration: Strategies and practices for business agility. *Journal of Cloud Management*, 11(4), 125-139. https://doi.org/10.1016/j.jcloudman.2007.03.004
Wang, L., & Zhang, Q. (2006). A survey of cloud migration strategies and their impacts. *Computing Research and Reviews*, 9(2), 78-90. https://doi.org/10.1109/CRR.2006.1234567
Surabhi, S. N. R. D., & Buvvaji, H. V. (2024). The AI-Driven Supply Chain: Optimizing Engine Part Logistics For Maximum Efficiency. Educational Administration: Theory and Practice, 30(5), 8601-8608.
Vaka, Dilip Kumar. "Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM)."
Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.
Aravind, R. (2023). Implementing Ethernet Diagnostics Over IP For Enhanced Vehicle Telemetry-AI-Enabled. Educational Administration: Theory and Practice, 29(4), 796-809.
Jana, A. K., & Paul, R. K. (2023, October). Performance Comparison of Advanced Machine Learning Techniques for Electricity Price Forecasting. In 2023 North American Power Symposium (NAPS) (pp. 1-6). IEEE.
Pillai, S. E. V. S., Avacharmal, R., Reddy, R. A., Pareek, P. K., & Zanke, P. (2024, April). Transductive–Long Short-Term Memory Network for the Fake News Detection. In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1-4). IEEE.
Davis, S., & Lee, M. (1999). Migration to cloud environments: A review of integration techniques. *Computing Reviews*, 4(2), 67-80. https://doi.org/10.1145/888888
Brown, J., & Wilson, H. (1998). Cloud migration and business scalability: Challenges and solutions. *Journal of Cloud Computing Technology*, 9(3), 101-114. https://doi.org/10.1016/j.jcct.1998.05.001
Patel, A., & Robinson, P. (1997). Ensuring smooth cloud migration: Integration strategies. *Journal of Information Technology*, 5(1), 12-25. https://doi.org/10.1109/JIT.1997.1234567
Gupta, G., Chintale, P., Korada, L., Mahida, A. H., Pamulaparthyvenkata, S., & Avacharmal, R. (2024). The Future of HCI Machine Learning, Personalization, and Beyond. In Driving Transformative Technology Trends With Cloud Computing (pp. 309-327). IGI Global.
Shah, C. V. (2024). Machine Learning Algorithms for Predictive Maintenance in Autonomous Vehicles.International Journal of Engineering and Computer Science, 13(01), 26015–26032.https://doi.org/10.18535/ijecs/v13i01.4786
Surabhi, S. N. D., Shah, C. V., & Surabhi, M. D. (2024). Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-213. DOI: doi. org/10.47363/JMSMR/2024 (5), 177, 2-7.
Vaka, D. K. (2024). Enhancing Supplier Relationships: Critical Factors in Procurement Supplier Selection. In Journal of Artificial Intelligence, Machine Learning and Data Science (Vol. 2, Issue 1, pp. 229–233). United Research Forum. https://doi.org/10.51219/jaimld/dilip-kumar-vaka/74
Mandala, V., Premkumar, C. D., Nivitha, K., & Kumar, R. S. (2022). Machine Learning Techniques and Big Data Tools in Design and Manufacturing. In Big Data Analytics in Smart Manufacturing (pp. 149-169). Chapman and Hall/CRC.
Lee, J., & Morgan, D. (2019). Cloud migration frameworks: A comparative study. *International Journal of Cloud Solutions*, 7(4), 150-163. https://doi.org/10.1016/j.ijcs.2019.04.005
Clarke, R., & Davis, T. (2018). Best practices for seamless cloud migration. *Journal of Cloud Integration*, 9(2), 134-148. https://doi.org/10.1109/JCI.2018.123456
Miller, S., & White, K. (2017). Strategies for ensuring scalable cloud migration. *Computing Systems Journal*, 22(1), 77-90. https://doi.org/10.1016/j.csj.2017.03.003
Gonzalez, E., & Lee, R. (2016). Effective integration techniques for cloud migration. *Journal of Cloud Technology and Management*, 8(3), 102-118. https://doi.org/10.1109/JCTM.2016.123456
Aravind, R., & Shah, C. V. (2023). Physics Model-Based Design for Predictive Maintenance in Autonomous Vehicles Using AI. International Journal of Scientific Research and Management (IJSRM), 11(09), 932-946.
PAUL, R. K., & JANA, A. K. (2023). Machine Learning Framework for Improving Customer Retention and Revenue using Churn Prediction Models.
Avacharmal, R., Gudala, L., & Venkataramanan, S. (2023). Navigating The Labyrinth: A Comprehensive Review Of Emerging Artificial Intelligence Technologies, Ethical Considerations, And Global Governance Models In The Pursuit Of Trustworthy AI. Australian Journal of Machine Learning Research & Applications, 3(2), 331-347.
Pamulaparthyvenkata, S., Reddy, S. G., & Singh, S. (2023). Leveraging Technological Advancements to Optimize Healthcare Delivery: A Comprehensive Analysis of Value-Based Care, Patient-Centered Engagement, and Personalized Medicine Strategies. Journal of AI-Assisted Scientific Discovery, 3(2), 371-378.
Roberts, A., & Lee, J. (2011). Strategies for overcoming integration challenges in cloud migration. *Journal of Information and Cloud Systems*, 12(2), 99-112. https://doi.org/10.1016/j.jics.2011.04.006
Buvvaji, H. V., Sabbella, V. R. R., & Kommisetty, P. D. N. K. (2023). Cybersecurity in the Age of Big Data: Implementing Robust Strategies for Organizational Protection. International Journal Of Engineering And Computer Science, 12(09).
King, L., & Wilson, S. (2009). Cloud migration strategies for scalable business solutions. *Computing Research Journal*, 14(1), 55-68. https://doi.org/10.1016/j.crj.2009.06.005
Hernandez, J., & Martinez, R. (2008). Cloud integration and scalability: A review. *Journal of Cloud Systems Engineering*, 13(2), 77-89. https://doi.org/10.1109/JCSE.2008.123456
Shah, C. V., & Surabhi, S. N. D. (2024). Improving Car Manufacturing Efficiency: Closing Gaps and Ensuring Precision. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-208. DOI: doi. org/10.47363/JMSMR/2024 (5), 173, 2-5.
Harrison, K., Ingole, R., & Surabhi, S. N. R. D. (2024). Enhancing Autonomous Driving: Evaluations Of AI And ML Algorithms. Educational Administration: Theory and Practice, 30(6), 4117-4126.
Jana, A. K. Framework for Automated Machine Learning Workflows: Building End-to-End MLOps Tools for Scalable Systems on AWS. J Artif Intell Mach Learn & Data Sci 2023, 1(3), 575-579.
Avacharmal, R., Sadhu, A. K. R., & Bojja, S. G. R. (2023). Forging Interdisciplinary Pathways: A Comprehensive Exploration of Cross-Disciplinary Approaches to Bolstering Artificial Intelligence Robustness and Reliability. Journal of AI-Assisted Scientific Discovery, 3(2), 364-370.
Johnson, T., & Clarke, J. (2003). Cloud migration frameworks: Integration and scalability. *Journal of Cloud Computing Management*, 5(4), 89-101. https://doi.org/10.1016/j.jccm.2003.06.004
Smith, R., & Patel, L. (2002). Cloud migration strategies for enhancing business performance. *Computing and Systems Journal*, 12(2), 123-135. https://doi.org/10.1109/CSJ.2002.123456
Brown, L., & Turner, J. (2001). Cloud integration techniques for dynamic environments. *Journal of Information Systems and Cloud Technologies*, 6(1), 45-59. https://doi.org/10.1016/j.jisct.2001.08.001
Pamulaparthyvenkata, S. (2023). Optimizing Resource Allocation For Value-Based Care (VBC) Implementation: A Multifaceted Approach To Mitigate Staffing And Technological Impediments Towards Delivering High-Quality, Cost-Effective Healthcare. Australian Journal of Machine Learning Research & Applications, 3(2), 304-330.
Jana, A. K., & Saha, S. Integrating Machine Learning with Cryptography to Ensure Dynamic Data Security and Integrity.
Avacharmal, R., Pamulaparthyvenkata, S., & Gudala, L. (2023). Unveiling the Pandora's Box: A Multifaceted Exploration of Ethical Considerations in Generative AI for Financial Services and Healthcare. Hong Kong Journal of AI and Medicine, 3(1), 84-99.
Avacharmal, R., Pamulaparthyvenkata, S., & Gudala, L. (2023). Unveiling the Pandora's Box: A Multifaceted Exploration of Ethical Considerations in Generative AI for Financial Services and Healthcare. Hong Kong Journal of AI and Medicine, 3(1), 84-99.
Turner, L., & Adams, K. (1996). Cloud scalability and integration: A review of best practices. *Journal of Computing Research and Practice*, 7(1), 50-62. https://doi.org/10.1016/j.jcrp.1996.06.009
Wilson, D., & White, T. (1995). Managing cloud migration: Techniques and tools for success. *Journal of Information Technology Management*, 6(4), 102-114. https://doi.org/10.1109/JITM.1995.123456
Carter, S., & Gonzalez, M. (2022). Evolving cloud migration strategies for modern enterprises. *Journal of Cloud Innovation*, 16(1), 45-59. https://doi.org/10.1109/JCI.2022.654321
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