Integrated Reinforcement Learning Scheduler for Intelligent Cloud Resource Optimization Using ML-RLSICRO

The effective allocation of cloud resources is a key challenge to address because of dynamic workloads, heterogeneous task requirements, and the requirement to minimize latency and maximize resource utilization. Conventional scheduling methods do not usually cope with the dynamic changes of large-scale distributed environments. A key issue in the current distributed computing environment is the allocation of cloud resources that have dynamic workloads, resource heterogeneity and the necessity to maximize performance and minimize energy consumption and operational cost. Unproductive scheduling systems can lead to underutilization of resources, latency, and lack of reliability in the system. This paper will attempt to solve these problems by introducing a Machine Learning-Based Reinforcement Learning-Based Scheduler to Intelligent Cloud Resource Optimization (ML-RLSICRO). The suggested framework includes data preprocessing and normalization, and feature optimization based on machine learning to identify the patterns of resource utilization of interest. Then, a reinforcement learning-based scheduling algorithm is applied to the environment to learn the optimal policies and dynamically allocate resources. The efficiency of the given model is also tested on the real-life cloud resource allocation data. Experimentally, it can be shown that ML-RLSICRO has a better testing accuracy of 95.32, and its precision, recall, and F1-score of 86.51, 84.83, and 85.59 are better than the current methods like Deep Reinforcement Learning, GWO, and PCRA. The results support the argument that the suggested approach has a high potential to offer scalable, robust, and intelligent solution to effective management of cloud resources in next-generation computing architectures.