Optimizing Resource Allocation in Cloud Computing Using AI Techniques

Cloud computing allows access to computing re-sources in a scalable and on-demand fashion, yet effective, resource allocation is also a major problem because of the dynamic workloads and unpredictable user demands. The com-mon allocation approaches, including the provisioning that is not dynamic and scheduling that is based on the rules, tend to lead to low utilization of resources, high latency, and high operational costs. To solve these problems, this paper suggests an AI-based solution to optimize the resources distribution in the cloud environment. The suggested system will use the methods of Artificial Intelligence, such as, but not limited to, Machine Learning, Deep Learning, and Reinforcement Learning, to predict the workload trends and dynamically distribute the resources. The predictive models are trained using historical data like CPU usage, memory consumption, and network bandwidth. These models help to pre-dict resource needs with accuracy and proactively and efficiently assign them. Reinforcement Learning also boosts decision-making as it continuously improves allocation policies through system feedback. Experimental findings show that the suggested solution can be used to better utilize resources and minimize latency and the overall system performance than conventional solutions. Scalability and adaptability in a large-scale cloud environment is also supported by the model. This paper has shown that the application of AI methods in cloud resource management is an efficient way to get effective, cost-efficient, and smart resource allocation.