Rolling Upgrades, Zero Downtime Modernizing SAP Infrastructure with Intelligent Automation
DOI:
https://doi.org/10.15662/IJEETR.2025.0706022Keywords:
Automated Dependency Orchestration, Dependency Blindness, Transaction Integrity Rate, Topological Awareness, Hidden Dependencies, Stateful Architecture, Dependency Risk Function, Legacy-Innovation ParadoxAbstract
The contemporary discourse on enterprise infrastructure modernization frequently conflates the migration of workloads with the transformation of operational logic, creating a dissonance between the fluidity of cloud-native methodologies and the rigidity of stateful legacy architectures. While recent literature emphasizes AI-driven predictive maintenance to enhance availability, these approaches often exhibit a fundamental “Dependency Blindness,” optimizing for server uptime while neglecting the complex, synchronous software couplings that dictate transactional integrity. To address this architectural tension, this study introduces a Dependency-Aware Intelligent Automation Framework, validated through rigorous fault-injection experiments on a live SAP landscape. We contrast this with the traditional “Big Flip” methodology which incurs a 50% capacity loss and standard rolling upgrades that risk breaking hidden dependencies. Empirical results indicate that while the proposed framework increases upgrade duration by approximately 15% compared to standard rolling methods, it reduces the Transaction Error Rate from a baseline of 8.3% to a negligible 0.04%, effectively eliminating the data corruption caused by premature node termination. These findings challenge the prevailing fixation on predictive hardware heuristics, arguing instead for a paradigm shift toward “Automated Dependency Orchestration” that prioritizes process continuity over mere infrastructure availability. Ultimately, this research demonstrates that achieving veritable zero downtime in legacy environments requires acknowledging that stability is a function of topological awareness rather than algorithmic speed.References
1. Dumitraș, T., & Narasimhan, P. (2009). Why Do Upgrades Fail and What Can We Do about It? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5897, 308-327. https://doi.org/10.1007/978-3-642-10445-9_18
2. Ng, K., Chen, C.-H., Lee, C. K. M., Jiao, R., & Yang, Z.-X. (2021). A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 50, 101246. https://doi.org/10.1016/j.aei.2021.101246
3. Malhotra, A., Elsayed, A. T. A., Torres, R., & Venkatraman, S. (2023). Evaluate Solutions for Achieving High Availability or Near Zero Downtime for Cloud Native Enterprise Applications. IEEE Access, 11, 10214005-10214018. https://doi.org/10.1109/ACCESS.2023.3303430
4. Basikolo, E., & Basikolo, T. (2023). Towards zero downtime: Using machine learning to predict network failure in 5G and beyond. Internet of Things and Advanced Technologies, 4(3), 11-20. https://doi.org/10.52953/pyaf8065
5. Bornet, P., Barkin, I., & Wirtz, J. (2021). Intelligent Automation: Welcome to the World of Hyperautomation - Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human. World Scientific. https://doi.org/10.1142/12239
6. Siderska, J., Aunimo, L., Süße, T., von Stamm, J., Kedziora, D., & Aini, S. N. B. M. (2023). Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends. Engineering Management in Production and Services, 15(4), 116-136. https://doi.org/10.2478/emj-2023-0030
7. Rakshit, H., & Banerjee, S. (2024). Scalability Evaluation on Zero Downtime Deployment in Kubernetes Cluster. 2024 IEEE Conference on Application & Innovation in Advanced Computing (CALCON). https://doi.org/10.1109/CALCON63337.2024.109140461
8. Malhotra, A., Elsayed, A., Torres, R., & Venkatraman, S. 2(2024). Evaluate Canary Deployment Techniques Using Kubernetes, Istio, and Liquibase for Cloud Native Enterprise Applications to Achieve Zero Downtime for Continuous Deployments. IEEE Access, 12, 97669-97686. https://doi.org/10.1109/ACCESS.2024.3416087
9. Rudrabhatla, C. K. (2020). Comparison of zero downtime based deployment techniques in public cloud infrastructure. 2020 International Conference on Smart Techniques in Engineering, Material Sciences and Allied Applications (I-SMAC), 60-65. https://doi.org/10.1109/I-SMAC49090.2020.9243605
10. Sun, D. W., Bass, L., Fekete, A., Gramoli, V., Tran, A. B., Xu, S., & Zhu, L. (2014). Quantifying Failure Risk of Version Switch for Rolling Upgrade on Clouds. 2014 IEEE International Conference on Big Data and Cloud Computing, 116-123. https://doi.org/10.1109/BDCloud.2014.16
11. Qureshi, M., Mahimkar, A., Qiu, L., Ge, Z., Zhang, M., & Broustis, I. (2017). Coordinating rolling software upgrades for cellular networks. 2017 IEEE International Conference on Network Protocols (ICNP), 1-10. https://doi.org/10.1109/ICNP.2017.8117537
12. Wolski, A., & Laiho, K. (2004). Rolling Upgrades for Continuous Services. Lecture Notes in Computer Science, 3169, 137-151. https://doi.org/10.1007/978-3-540-30225-4_13
13. Bhusal, N., Abdelmalak, M., Kamruzzaman, M., & Benidris, M. (2020). Power System Resilience: Current Practices, Challenges, and Future Directions. IEEE Access, 8, 21544-21575. https://doi.org/10.1109/ACCESS.2020.2968586
14. Endo, P., Rodrigues, M., Gonçalves, G., Kelner, J., Sadok, D., & Curescu, C. (2016). High availability in clouds: systematic review and research challenges. Journal of Cloud Computing: Advances, Systems and Applications, 5(1), 1-13. https://doi.org/10.1186/s13677-016-0066-8
15. Sarker, I. H. (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, 3(4), 1-28. https://doi.org/10.1007/s42979-022-01043-x
16. Zuo, M. (2021). System reliability and system resilience. Science and Engineering of Smart Systems, 1(1), 17-23. https://doi.org/10.1007/s42524-021-0176-y
17. Hosseini, S., Barker, K., & Ramírez-Márquez, J. (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47-61. https://doi.org/10.1016/j.ress.2015.08.006
18. Corrales-Estrada, A. M., Gómez-Santos, L., Bernal-Torres, C. A., & Rodriguez-López, J. E. (2021). Sustainability and Resilience Organizational Capabilities to Enhance Business Continuity Management: A Literature Review. Sustainability, 13(15), 8196. https://doi.org/10.3390/su13158196
19. Brás, J., Pereira, R. d. C. d. F., & Moro, S. (2023). Intelligent Process Automation and Business Continuity: Areas for Future Research. Informatics, 14(2), 122. https://doi.org/10.3390/info14020122
20. Al-Subaiei, W., Al-Herz, E., Al-Marri, W., Al-Otaibi, R., Ashyan, H., & Jaber, H. (2021). Industry 4.0 Smart Predictive Maintenance in the Oil Industry to Enable Near-Zero Downtime in Operations. Proceedings of the International Conference on Industrial Engineering and Operations Management, 3121-3132. https://doi.org/10.46254/an11.20211234
21. Azadegan, A., Parast, M., Lucianetti, L., Nishant, R., & Blackhurst, J. (2020). Supply Chain Disruptions and Business Continuity: An Empirical Assessment. Decision Sciences Journal, 51(6), 1638-1678. https://doi.org/10.1111/DECI.12395
22. Saxena, D., & Singh, A. K. (2022). A High Availability Management Model Based on VM Significance Ranking and Resource Estimation for Cloud Applications. IEEE Transactions on Services Computing, 16(1), 118-132. https://doi.org/10.1109/TSC.2022.3206417
23. Bathla, G., Bhadane, K., Singh, R., Kumar, R., Aluvalu, R., Krishnamurthi, R., Kumar, A., Thakur, R. N., & Basheer, S. (2022). Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities. Mathematical Problems in Engineering, 2022, 1-17. https://doi.org/10.1155/2022/7632892
24. Dowd, Z., Franz, A. Y., & Wasek, J. S. (2020). A Decision-Making Framework for Maintenance and Modernization of Transportation Infrastructure. IEEE Transactions on Engineering Management, 67(1), 22-35. https://doi.org/10.1109/TEM.2018.2870326
