A Hybrid Machine Learning Model for Analyzing the Dynamic Behavior of the Cloud Data for Optimal Resource Allocation and Scheduling to Enhance Cost Optimization

The challenges of efficient resource allocation and scheduling in cloud computing and the dynamic and unpredictable nature of cloud data, which leads to suboptimal cost management, continue to be serious problems. The aim of this work is to devise a machine learning model-that is a hybrid-as the best solution for resource allocation and scheduling in cloud computing so that the costs can be minimized in a dynamic cloud environment while working efficiently. This differs from the traditional approaches that have trouble dealing with several factors at the same time. Instead, this work utilizes both supervised and reinforcement learning methodologies so as to devise an integrated solution. In detail, the Long Short-Term Memory (LSTM) networks are employed to provide an accurate forecast of the workload's time series while the Deep Q-Networks (DQN) allow for smart decision-making on how to distribute the resources in the best way. The system is always on the lookout for cloud operations, not just gathering real-time data on the workload fluctuations but also on the resource requests and utilization patterns to build an adaptive scheduling model, which in turn leads to enhancements in cost-efficient service quality. The experiment outcomes validate the model presented in this paper as it manages effectively the problem of underutilization and over-provisioning with a 25 % reduction in costs compared to the traditional methods of scheduling. This work merges predictive analytics and intelligent resource management, thus facilitating cloud computing to be better at cost, scalability, and high-performance in highly dynamic environments.