International Research Journal of Advanced Engineering and Technology

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International Research Journal of Advanced Engineering and Technology

Article Details Page

Performance Engineering and Intelligent Automation in Cloud-Accelerated and Data-Intensive Enterprise Architectures: A Synthesis of Emerging Trends

Authors

  • Dr. Rhys A. Vardon Department of Computing Systems Engineering, Dublin Institute for Advanced Technology, Dublin, Ireland
  • Prof. Elena K. Petrov Department of Computing Systems Engineering, Dublin Institute for Advanced Technology, Dublin, Ireland

DOI:

https://doi.org/10.55640/

Keywords:

Performance Engineering, Intelligent Automation, Cloud Architectures, Firmware Optimization

Abstract

Purpose: This article synthesizes emerging trends at the intersection of performance engineering and intelligent automation, analyzing their critical role in shaping modern cloud-accelerated and data-intensive enterprise architectures. The goal is to provide a unified framework demonstrating the necessity of vertical optimization, from firmware to enterprise workflows.

Methodology: A systematic synthesis approach was employed, integrating specialized domain insights across three core areas: Foundational Performance (firmware-level optimization, network scaling), System Quality (proxy-based thermal management, factory-grade diagnostics), and Intelligent Workflow Automation (CICD for financial data, AI in content management, cloud orchestration simulation). The analysis weaves together key architectural innovations to highlight their systemic dependencies.

Findings: The synthesis reveals that optimal performance hinges on firmware-level optimization for models like LLMs [2] and managing physical constraints via proxy-based evaluation for cloud GPUs [6]. Reliability is guaranteed by factory-grade diagnostic automation [5] and robust testing via simulation tools [7]. Furthermore, the strategic value of automation extends from secure CICD pipelines [3] and AI-integrated Enterprise Content Management [8] to extending the digital footprint into the physical world through "BIM-to-Field" workflows [4].

Originality/Value: This work provides a novel architectural blueprint, arguing that the future enterprise system requires the inseparable convergence of deep, multi-layered performance engineering and pervasive intelligent automation to achieve unparalleled scale, reliability, and strategic data utility.

References

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Chandra, R. (2025). Reducing latency and enhancing accuracy in LLM inference through firmware-level optimization. International Journal of Signal Processing, Embedded Systems and VLSI Design, 5(2), 26–36. https://doi.org/10.55640/ijvsli-05-02-02

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Durgam, S. (2025). CICD automation for financial data validation and deployment pipelines. Journal of Information Systems Engineering and Management, 10(45s), 645–664. https://doi.org/10.52783/jisem.v10i45s.8900

Enugala, V. K. (2025). “BIM-to-Field” inspection workflows for zero paper sites. Utilitas Mathematica, 122(2), 372–404. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2711

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Published

2025-10-19

How to Cite

Performance Engineering and Intelligent Automation in Cloud-Accelerated and Data-Intensive Enterprise Architectures: A Synthesis of Emerging Trends. (2025). International Research Journal of Advanced Engineering and Technology, 2(10), 57-65. https://doi.org/10.55640/

How to Cite

Performance Engineering and Intelligent Automation in Cloud-Accelerated and Data-Intensive Enterprise Architectures: A Synthesis of Emerging Trends. (2025). International Research Journal of Advanced Engineering and Technology, 2(10), 57-65. https://doi.org/10.55640/

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