Artificial Intelligence for Resource Optimization in Cloud Computing Environments

Abstract

Scalable resources are provided by cloud computing, but the effective use of the resource is a major issue associated with dynamic workloads and complicated scheduling requirements . Recent progress in the field of artificial intelligence (AI) offers the prospects of optimization in the process of resources allocation in clouds by allowing managing the intelligent usage of CPU resources and enhancing the efficiency of tasks scheduling. This paper suggests an AI-based framework of optimizing the cloud resources that actively uses machine learning and reinforcement learning methods to distribute loads dynamically and schedule tasks. The suggested algorithm is tested in the context of simulated cloud conditions, showing better CPU and task scheduling performance ([?]80% and 90% respectively) than a control heuristic algorithm ([?]50% CPU utilization, 70%). The Results and Discussion elaborate on the way in which AI-based scheduling reduces idle CPU time and minimizes scheduling delays. This study demonstrates the possibility of the AI to assist in the improvement of the cloud resource management, which creates the basis of the intelligent autoscaling and schedule of cloud platforms, in general.