Year: 2025
Type: article
Abstract: The exponential expansion of cloud infrastructure has created unprecedented complexity in Software-Defined Data Centers, pushing traditional management approaches beyond their effective limits. This comprehensive article examines how Artificial Intelligence and Machine Learning technologies fundamentally transform data center automation through intelligent, adaptive capabilities. Integrating advanced technologies addresses critical challenges in modern cloud environments, including inefficient knowledge management, labor-intensive scripting, and reactive maintenance approaches. Natural Language Processing significantly enhances knowledge retrieval by enabling contextaware searches that transcend keyword limitations. At the same time, intelligent automation systems generate infrastructure configurations from highlevel descriptions, dramatically reducing deployment times and error rates. Reinforcement learning algorithms continuously optimize resource allocation and performance, adapting to environmental variations with minimal human guidance. Predictive analytics capabilities enable proactive identification of potential issues before service degradation occurs, substantially reducing downtime and resolution times. The transformation extends beyond technology to encompass organizational considerations, including implementation strategies, data governance requirements, cross-functional collaboration models, and comprehensive change management approaches essential for realizing the full potential of AI-augmented cloud operations.
Author: Shaileshbhai Revabhai Gothi
Institution: University of Florida
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409395725