Year: 2025
Type: article
Abstract: This article explores the transformative impact of artificial intelligence on cloud network capacity planning and predictive maintenance, examining how these technologies are reshaping traditional approaches to infrastructure management. The article investigates the evolution from reactive to proactive operational models, where advanced predictive analytics enable cloud providers to anticipate demand fluctuations and potential system failures before they impact service delivery. The article identifies how neural networks, time-series algorithms, and vector databases are deployed to optimize resource allocation, balance workloads across multi-cloud environments, and detect emerging performance issues. The article highlights significant advancements in prediction accuracy and efficiency gains while acknowledging persistent challenges in model explainability, cross-environment transferability, and ethical governance. The article presents empirical evidence demonstrating the effectiveness of hybrid AI approaches that combine statistical methods with deep learning, particularly when implemented through cross-functional teams using incremental deployment strategies. The article examines promising research directions, including federated learning, neuro-symbolic AI, and edge computing integration. It concludes that successful AI implementation in cloud network management requires both technological sophistication and organizational adaptation to realize its full potential for improving infrastructure reliability while reducing operational costs
Author: Anil Kumar Gottepu
Institution: Akamai (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411243659