Advanced computational model for energy-efficient resource allocation in cloud computing environments

Cloud computing uses resource allocation techniques to maximize compute efficiency while preserving energy usage. Energy-efficient models reduce environmental exposure while maintaining efficiency using sophisticated algorithms and dynamic scheduling of workloads. Current models face challenges such as inefficiency in dynamically managing workloads, poor scalability in large-scale cloud environments, and high computational complexity, which hinders real-time decision-making and energy savings. To overcome these shortcomings, this research suggests an Effective Fire Hawk search-based Deep Q Network (EFH-DQN) approach, combining optimization and reinforcement learning to improve energy-efficient resource allocation and Quality of Service (QoS) in cloud infrastructures. EFH searches and optimizes the initial resource allocation using its exploration function, whereas DQN adapts resource allocation dynamically using real-time system performance and feedback. The dataset includes network measurements, such as bandwidth, power consumption, distances, state-action pairs, and rewards for bandwidth usage and energy conservation. The model is tested in a 250-node cloud environment with a reduction in energy consumption of 370 kWh, a 130 ms decrease in latency, and an improvement in throughput to 1,050 tasks per second. These are evidence of the success of the model in balancing resource allocation for energy efficiency and performance. By eliminating the primary limitations in current resource allocation techniques, EFH-DQN offers a flexible and scalable solution to energy-efficient cloud computing. The results acknowledge its potential to minimize operational expenses, lower environmental footprints, and provide maximum quality of service in large-scale cloud systems. The proposed framework provides an excellent improvement in resource management, ensuring sustainability and effectiveness in dynamic clouds.