AI-Driven DevOps for Cloud ERP Data Systems: Software Engineering Evaluation and DC–DC Converter-Optimized Performance for Financial Applications

Saravana Muthu Kumar

doi:10.15662/IJRAI.2024.0706007

Authors

Saravana Muthu Kumar Independent Researcher, Maryland, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0706007

Keywords:

AI-driven DevOps, Cloud-native ERP, DC–DC converter, Software engineering, Sustainable computing, Power optimization, GaN technology, CI/CD pipelines, Energy efficiency, Cloud infrastructure

Abstract

Modern Enterprise Resource Planning (ERP) systems are increasingly transitioning to cloud-native environments, powered by artificial intelligence (AI) and DevOps methodologies. This evolution addresses the growing demands for scalability, flexibility, and real-time analytics. However, with the computational intensity of AI-driven workloads, energy consumption in ERP systems has risen significantly. This paper proposes a comprehensive evaluation framework that integrates AI-driven DevOps within cloud-native ERP systems while incorporating energy optimization through DC–DC power converter technologies. The primary goal is to assess the performance, efficiency, and software engineering implications of such an architecture.

The framework includes AI-enhanced automation across the software lifecycle—from development and deployment to monitoring—alongside intelligent workload scheduling based on energy metrics. The use of advanced GaN-based DC–DC converters in the underlying infrastructure enables a more sustainable operation, particularly in high-performance computing environments. Through a mixed-method research design, this study evaluates the software engineering processes, deployment efficiency, and power consumption characteristics of the proposed system.

The literature review reveals limited integration between software-centric DevOps practices and hardware-level energy optimizations, underscoring the novelty and necessity of the proposed approach. Empirical results show significant improvements in deployment velocity, system responsiveness, and energy efficiency. Key advantages include enhanced automation, reduced downtime, and improved carbon footprint, while challenges include complexity in integration and initial setup costs.

This research contributes to the interdisciplinary discourse between software engineering, cloud computing, and sustainable infrastructure. Future work involves the incorporation of quantum-resilient security mechanisms and the extension of the framework to edge-based ERP deployments. By aligning software development practices with energy-aware hardware configurations, this study charts a path toward intelligent, sustainable enterprise solutions.

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