SUSTAINABLE GREEN COMPUTING APPROACHES FOR MANAGING HIGH-DENSITY ARTIFICIAL SYSTEMS
Abstract
Purpose: This paper examines the ways in which green computing can be practiced sustainably to enhance environmental and operational performance of high density AI systems. The study centers on energy-saving hardware, high-tech cooling, renewable energy, and smart workload optimization and explores the mediation of the effects by green data center management. Methodology: The quantitative design was employed, which comprised the collection of data among 350 IT managers and data center administrators who work with high-density AI infrastructures. The questionnaire that was used contained validated scales and involved the participants in a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4 was used to test the proposed conceptual model and hypotheses, which provided an opportunity to test the measurement reliability and the structural relationships between the constructs. Results: The analysis indicates that green data center management is heavily dependent on the practices of green computing and supports better performance of the AI system. The integration of renewable energy and energy-saving hardware produce the largest positive impact on sustainable operations, and the optimization of the workload intelligently and high-tech cooling technologies further increase the efficiency of the system. This paper validates that the relationship between sustainable AI performance and green computing practices is mediated by green data center management. Implications: The results contribute both to the existing empirical literature on the topic of Green AI and sustainable computing and show that integrated green infrastructure can significantly improve the environmental sustainability of AI systems. In practice, the findings highlight the importance of switching to renewable-powered data centers, energy-efficient hardware designs, and smart-resource management. The insights are useful in assisting organizations, policy makers, and developers to create sustainable AI ecosystems that can accommodate increasing computational needs and reduce environmental impact.
Keywords : Green Computing, Sustainable Artificial Intelligence, Green Data Center Management, Energy-Efficient AI Infrastructure, Intelligent Workload Optimization Sustainable Digital Infrastructure.
