Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence

<ns3:p> Background The high variability of workloads makes it very difficult for cloud datacenters to efficiently schedule their tasks and resource allocation. The correct forecasting of future resource utilization allows us to proactively scale and implement more sophisticated scheduling policies, which eventually results in improved resource utilization and fewer failures. Methods This study uses the Google Cluster Trace v3 dataset, which contains a wealth of job and task data (start and end times, CPU and memory usage, scheduling class, and priority) to create supervised machine-learning models that can predict future CPU usage. Conclusions In this study the regression algorithms evaluated were Linear Regression, Support Vector Regression, Random Forest, which were evaluated using a confusion matrix. Our results show that higher-order models, especially neural network models, are quite successful in predicting computer usage (94% validation accuracy and R <ns3:sup>2</ns3:sup> of 0.90). These results underscore the potential of machine learning-based CPU demand predictions as valuable tools for cloud schedulers, improving resource management, and reducing operational costs. Also discuss the feasibility of deploying the proposed solution on distributed platforms such as Spark and Google Cloud and outline future research directions to integrate predictive models with real-time cloud resource management. </ns3:p>