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Automating Data Center Lifecycle Management: A Comprehensive Framework for Enhanced Operational Excellence

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  • Shaileshbhai Revabhai Gothi

Abstract

Data center infrastructure forms the backbone of modern digital transformation initiatives, necessitating sophisticated management approaches throughout their lifecycle. As complexity increases, traditional manual operations have become unsustainable, leading to significant operational challenges including frequent outages, inefficient resource utilization, and security vulnerabilities. The global data center automation market is expanding rapidly, with projections indicating growth from $9.45 billion in 2024 to over $20 billion by 2028. This comprehensive article explores the evolution of data center management strategies, identifying critical limitations of manual approaches and documenting the emergence of increasingly sophisticated automation frameworks. Four key automation strategies are analyzed: Infrastructure as Code for standardized provisioning, event-driven automation for self-healing environments, API-driven orchestration for heterogeneous integration, and AI-powered predictive analytics for optimized operations. A structured implementation framework addressing technical, process, and organizational dimensions provides a roadmap for successful adoption. Real-world case studies across financial services, healthcare, and retail sectors demonstrate tangible benefits including cost reduction, enhanced security posture, improved operational agility, and optimized resource utilization. The documented improvements—spanning from 25-85% across various operational metrics—illustrate how automation emerges as an essential strategic enabler for organizations seeking to maximize the value of their data center investments while meeting evolving business requirements.

Suggested Citation

  • Shaileshbhai Revabhai Gothi, 2025. "Automating Data Center Lifecycle Management: A Comprehensive Framework for Enhanced Operational Excellence," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 2836-2846, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1326
    DOI: 10.32628/CSEIT25112748
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112748
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