Adaptive Neural Networks for Dynamic Resource Allocation in Cloud Computing Environments

Abstract

This paper presents a novel approach to dynamic resource allocation in cloud computing environments using adaptive neural networks. With the increasing demand for scalable and efficient cloud services, traditional resource management systems face challenges in handling dynamic workloads and optimizing performance. The proposed model integrates neural network-based algorithms to predict and adjust resource allocation in real-time, ensuring optimal utilization of cloud resources while maintaining high service quality. The system is designed to learn and adapt to varying workload patterns, improving efficiency and reducing response times. Simulations demonstrate significant improvements in resource allocation and overall system performance compared to conventional methods.