Abstract

The exponential growth of cloud computing has transformed how organizations manage and deploy IT resources, but this progress comes at a significant energy cost. Data centers, the backbone of cloud services, consume vast amounts of electricity, leading to high operational costs and environmental concerns. Energy-efficient resource allocation algorithms have emerged as a crucial solution for optimizing energy consumption while maintaining service quality. This paper provides a comprehensive survey of energy-efficient algorithms for cloud resource allocation in data centers, examining their principles, methodologies, and effectiveness. We analyze heuristic, metaheuristic, and machine learning-based approaches, with a particular focus on reinforcement learning and intelligent scheduling. The paper concludes with a discussion of challenges, future research directions, and the potential for sustainable cloud computing.