(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
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.