Author
Listed:
- 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
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1326. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.