(untitled) (International Journal of Engineering and Computer Science, 2025)
Cloud computing has revolutionized digital infrastructure, offering scalable and flexible access to resources. However, managing resources efficiently while minimizing energy consumption and reducing latency remains a significant challenge. This research proposes advanced algorithms for optimizing cloud computing performance, focusing on Dynamic Resource Management and Adaptive Scheduling Using Neural Networks. The dynamic resource management algorithm allocates resources in real-time based on demand, ensuring efficient usage and energy savings. The adaptive scheduling algorithm uses neural networks to predict future demand and optimize task distribution, improving response times and load balancing. Experimental results show a 10% to 15% reduction in response time and up to a 15% decrease in energy consumption, demonstrating the effectiveness of the proposed algorithms. These findings highlight the potential of intelligent algorithms to enhance cloud computing efficiency and sustainability.