Green computing becomes another important aspect of sustainable technology from the standpoint of energy usage management as the provision of energy-efficient functionality of data centers is essential to deal with environmental issues. In this study, we propose an evolution of energy efficient data centers via AI-based integration. Adopting a use-case driven methodology, this study creates and tests AI modularized models that respond to dynamically forecast workloads, predict energy demand loads, and optimize active cooling systems. With the help of machine learning algorithms, real-time monitoring and predictive analytics, this methodology can identify opportunities for energy blanketing, allowing for a significant decrease in energy consumption without affecting performance. Multi data center experiments showing a measured PUE (power usage effectiveness) and Carbon reduction An AI-driven dynamic cooling strategy reduced energy consumption by as much as 25% and intelligent workload distribution improved system efficiency by 15%. These results highlight the ability of AI to dramatically reduce the environmental impact of high energy computing systems, helping to ensure sustainable growth of data center operations in the future. Finally, this paper ends with the broader ramifications where green computing advances across multiple sectors like the energy-intensive AI, advocating similar efforts for other energy-intensive industries in line with global sustainability objectives. This also highlights the importance of collaboration between academia and industry to advance cleaner computing technologies more broadly.
Rambabu et al. (Wed,) studied this question.