AI-Driven Secure Cloud Ecosystem for Software Engineering: Oracle EBS Integration with Efficiency-Oriented Markov Decision Processes and a Scalable Blueprint Optimization Model
DOI:
https://doi.org/10.15662/IJARCST.2023.0603004Keywords:
Oracle E-Business Suite, cloud-native, interpretable AI, privacy-preserving machine learning, DC–DC converters, power-aware control, policy-as-code, federated learning, explainable AI, secure integrationsAbstract
Enterprises increasingly couple cloud-hosted enterprise applications (notably Oracle E-Business Suite, EBS) with operational technology (OT) such as DC–DC converters that manage power distribution in data centers and critical facilities. This convergence offers opportunities for cross-layer optimization—aligning procurement and maintenance workflows with energy-aware control—but also creates security, privacy, and safety challenges. We propose a secure cloud ecosystem architecture that tightly integrates Oracle EBS metadata and workflows with interpretable AI and privacy-preserving machine learning to deliver auditable, low-risk advisories for power-aware DC–DC converter management and for secure software engineering practices. Core components are: (1) non-invasive EBS connectors that surface asset, maintenance, and procurement context; (2) an interpretable-AI layer (rule lists, GAMs, and local explanations) that produces human-understandable recommendations; (3) a privacy-preserving ML pipeline (federated learning, secure aggregation, and differential privacy) enabling cross-site learning without centralizing sensitive telemetry; (4) a policy-as-code enforcement plane that compiles safety and compliance rules into verifiable guards; and (5) an immutable provenance and audit layer linking EBS events, model versions, and edge control actions.
The system emphasizes an advisory-first posture: ML-suggested setpoints and maintenance priorities are presented with clear explanations and uncertainty bands; closed-loop automation is permitted only after multi-stage approvals and formal safety checks. For DC–DC converters, edge-first control loops retain deterministic fast regulation while cloud-side models provide supervisory advisories for energy optimization and maintenance scheduling. Security controls include mutual-TLS, tokenized identifiers, encrypted registries, and forensics-ready immutable logs. Evaluation uses offline replayed telemetry, synthetic fault-injection, and shadow pilots to measure predictive accuracy, privacy leakage (ε-differential privacy), energy savings, operator acceptance, and forensic completeness.
Expected benefits include near-centralized model performance with reduced data movement, improved operator trust via interpretability, measurable energy and maintenance-efficiency gains, and auditable, privacy-aware cross-site learning. Trade-offs include latency and computational cost for privacy mechanisms, engineering complexity for EBS–OT mapping, and governance overhead to reconcile immutability with legal deletion requests. We provide a practical roadmap for staged adoption, MLOps governance, and operator training to deploy this secure cloud ecosystem in regulated environments.
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