Energy-Efficient Cloud Computing Solutions for Large-Scale Web Platforms

Energy efficiency is a critical concern for cloud computing infrastructures, especially for large-scale web platforms with extensive computational demands. This paper presents a framework integrating dynamic resource allocation, workload optimization, and renewable energy utilization to enhance energy efficiency without compromising performance or Quality of Service (QoS). By leveraging machine learning models for predictive resource management and bio-inspired algorithms for workload distribution, the framework achieves a 20% reduction in energy consumption, 85% resource utilization, and 95% QoS compliance. Additionally, it relies on renewable energy for 60% of operations, contributing to sustainability. The framework was evaluated using real-world datasets and simulated environments, demonstrating its scalability and adaptability to diverse workloads. This study addresses challenges in balancing energy efficiency and performance in cloud infrastructures, providing insights into intelligent optimization techniques and sustainable practices. Future work will extend the framework to emerging paradigms like edge and fog computing, ensuring relevance in next- generation distributed systems.