Enhancing Energy Efficiency in Cloud Computing Operations Through Artificial Intelligence

Abstract

The rapid expansion of cloud computing has led to substantial energy consumption, raising concerns regarding environmental sustainability. This study explores the potential of artificial intelligence (AI) to enhance energy efficiency in cloud computing operations. We investigate how advanced AI techniques, including machine learning algorithms and predictive analytics, can be employed to improve resource allocation, reduce power consumption, and boost overall system performance. Our study provides a detailed examination of AI-driven strategies for energy optimization, highlighting their ability to forecast demand, dynamically adjust resource provisioning, and implement energy-saving measures. We discuss the benefits of these AI applications in curbing energy consumption as well as the challenges associated with their deployment, such as data infrastructure requirements, algorithmic complexity, and integration with existing systems. By analyzing these factors, we demonstrate that AI can lead to significant energy savings while maintaining high service quality in cloud environments. This study underscores the potential of AI to drive both environmental sustainability and operational efficiency in the cloud computing sector, offering insights into future advancements and best practices for energy management.