Green cloud computing: AI for sustainable database management

The exponential growth of digital data has intensified the demand for cloud computing resources, leading to increased energy consumption and environmental concerns. Traditional cloud data centers operate at high energy levels, contributing significantly to carbon emissions and escalating operational costs. Green Cloud Computing (GCC) has emerged as a sustainable solution that integrates energy-efficient technologies, renewable energy sources, and artificial intelligence (AI) to optimize cloud infrastructure. By leveraging AI-driven algorithms, sustainable database management in GCC enhances resource allocation, workload distribution, and predictive maintenance, reducing the overall energy footprint while maintaining performance efficiency. This study explores the role of AI in sustainable database management within the GCC framework, focusing on intelligent workload scheduling, dynamic resource provisioning, and energy-efficient data storage techniques. AI-driven optimization models, such as reinforcement learning and deep learning-based predictive analytics, enable real-time adaptation to fluctuating workloads, ensuring minimal energy wastage. Furthermore, techniques like deduplication, compression, and auto-scaling enhance data storage efficiency while reducing redundancy. However, implementing AI for green cloud management presents challenges, including computational overhead, data security risks, and the need for regulatory compliance. Through an in-depth analysis of case studies and industry best practices, this research highlights how AI-driven sustainable database management can balance environmental responsibility with high-performance computing. The findings advocate for a holistic approach, combining AI innovations, regulatory frameworks, and green infrastructure investments to achieve carbon-neutral cloud ecosystems. By adopting AI-powered sustainability strategies, cloud providers and enterprises can significantly reduce their carbon footprint, ensuring a greener and more resilient digital future.

Authors

Institutions

Publication Details

Journal
World Journal of Advanced Research and Reviews
Published
2024-09-30
DOI
https://doi.org/10.30574/wjarr.2024.23.3.2611
Citations
14
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
7.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Green cloud computing: AI for sustainable database management

Oluwafemi Oloruntoba
14 citations
World Journal of Advanced Research and Reviews
Big Data and Business Intelligence
7.40
article

Green cloud computing: AI for sustainable database management

Oluwafemi Oloruntoba
article en
14 citations

Abstract

The exponential growth of digital data has intensified the demand for cloud computing resources, leading to increased energy consumption and environmental concerns. Traditional cloud data centers operate at high energy levels, contributing significantly to carbon emissions and escalating operational costs. Green Cloud Computing (GCC) has emerged as a sustainable solution that integrates energy-efficient technologies, renewable energy sources, and artificial intelligence (AI) to optimize cloud infrastructure. By leveraging AI-driven algorithms, sustainable database management in GCC enhances resource allocation, workload distribution, and predictive maintenance, reducing the overall energy footprint while maintaining performance efficiency. This study explores the role of AI in sustainable database management within the GCC framework, focusing on intelligent workload scheduling, dynamic resource provisioning, and energy-efficient data storage techniques. AI-driven optimization models, such as reinforcement learning and deep learning-based predictive analytics, enable real-time adaptation to fluctuating workloads, ensuring minimal energy wastage. Furthermore, techniques like deduplication, compression, and auto-scaling enhance data storage efficiency while reducing redundancy. However, implementing AI for green cloud management presents challenges, including computational overhead, data security risks, and the need for regulatory compliance. Through an in-depth analysis of case studies and industry best practices, this research highlights how AI-driven sustainable database management can balance environmental responsibility with high-performance computing. The findings advocate for a holistic approach, combining AI innovations, regulatory frameworks, and green infrastructure investments to achieve carbon-neutral cloud ecosystems. By adopting AI-powered sustainability strategies, cloud providers and enterprises can significantly reduce their carbon footprint, ensuring a greener and more resilient digital future.

World Journal of Advanced Research and ReviewsVol. 23(3)
Lamar University (US)
Openalex Percentile: Top 3%
Big Data and Business Intelligence
7.40
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.

Green cloud computing: AI for sustainable database management — Oluwafemi Oloruntoba · World Journal of Advanced Research and Reviews (2024) | TGRS Research Map | TGRS