AI-Driven Resource Allocation: Revolutionizing Cloud Infrastructure Management

This comprehensive article examines the transformative impact of artificial intelligence on cloud resource management, exploring the evolution from traditional static allocation methods to dynamic, AI-driven approaches. The article investigates core technologies, including machine learning models and real-time decision-making frameworks, while evaluating their applications across virtual machine provisioning, container orchestration, and multi-cloud environments. Through detailed case studies of e-commerce platforms and video streaming services, the article demonstrates significant improvements in resource utilization, cost optimization, and service reliability. The article further addresses technical and operational challenges, including model overhead and system complexity, providing insights into the implementation considerations for organizations adopting AI-driven cloud management solutions.

Authors

Institutions

Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-02-03
DOI
https://doi.org/10.32628/cseit251112194
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Driven Resource Allocation: Revolutionizing Cloud Infrastructure Management

Swaroop K. Sood
1 citations
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Cloud Computing and Resource Management
3.25
article

AI-Driven Resource Allocation: Revolutionizing Cloud Infrastructure Management

Swaroop K. Sood
article en
1 citations

Abstract

This comprehensive article examines the transformative impact of artificial intelligence on cloud resource management, exploring the evolution from traditional static allocation methods to dynamic, AI-driven approaches. The article investigates core technologies, including machine learning models and real-time decision-making frameworks, while evaluating their applications across virtual machine provisioning, container orchestration, and multi-cloud environments. Through detailed case studies of e-commerce platforms and video streaming services, the article demonstrates significant improvements in resource utilization, cost optimization, and service reliability. The article further addresses technical and operational challenges, including model overhead and system complexity, providing insights into the implementation considerations for organizations adopting AI-driven cloud management solutions.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(1)
Microsoft (United States) (US)
Openalex Percentile: Top 11%
Cloud Computing and Resource Management
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AI-Driven Resource Allocation: Revolutionizing Cloud Infrastructure Management — Swaroop K. Sood · International Journal of Scientific Research in Computer Science Engineering and Information Technology (2025) | TGRS Research Map | TGRS