Artificial Intelligence for Resource Optimization in Cloud Data Centers: Exploring Sustainability Challenges

Abstract

There are concerns over cloud computing infrastructure as whether it could be sustainable for exponential growth of AI workloads. In this era, the present research examines the trends on energy consumption on cloud data centers and proposes that the use of energy efficient modules can help reduce the operational energy usage by approximately 70% without compromising the quality of service. This study suggests a holistic approach to green computing in the cloud for AI applications, which includes smart resource management, optimization of hardware and the use of modern cooling technologies. Based on industry facts, empirical evidence and key cloud provider reports, this study examines the priority issues in the current infrastructure and suggests solutions to these issues on the basis of empirical evidence. It is uncovered in the findings that more than 15% of energy is used by AI data centers and they are projected to consume up to 50% by 2030. It is clear there is a high level of consumption and fast action is required. This study evaluates the benefits of dynamic allocation of resources, virtualization, specialized AI accelerators, energy-aware scheduling and adoption of green energy. The study results indicate that the results of using several techniques in combination along with integrated techniques yield excellent results compared to interferences separately. The performance of a TPU based system is better than a general-purpose GPU, and AI-based cooling technology can reduce electricity consumption by approximately 40%. In addition, from on-premise to optimized cloud structure, migration can minimize carbon emission by 80%. This research contributes valuable, concrete and theoretical strategies for sustainable application of AI in cloud data centers, fulfilling a growing demand for computing.