A New Approach to Cloud Resource Scheduling Using Genetic Reinforcement Kernel Optimization and Machine Learning Model

ABSTRACT Two of the most essential elements of cloud resource management systems are resource management as well as scheduling. Due to the heterogeneity of resources, their interdependencies, and the unpredictable nature of load in a cloud environment, cloud Resource Scheduling (RS) is the most important issue to handle effectively. One of the most challenging tasks in cloud computing is RS, where resources must be assigned to the necessary tasks or jobs in accordance with the necessary Quality of Service (QoS) of the cloud applications. This study suggests a unique method for scheduling cloud resources based on a virtual data center and machine learning model. The genetic reinforcement kernel swarm optimization methodology and cloud data centers are deployed here. The suggested network analysis aims to balance energy usage and SLA. The suggested framework is assessed by doing experiments on the Google Cluster dataset, Planet Lab, and Bitbrains VM traces, as well as three real‐world workload datasets. Execution cost, Execution time, Makespan, Energy consumption, Resource utilization, and Scalability are factors that were examined here. The proposed framework is superior to several performance indicators compared to state‐of‐the‐art techniques. The project's methodology addresses the issues of load imbalance and excessive migration expenses.