Hybrid Machine Learning Integration with Task Scheduling Algorithms in Cloud Environments

Effective task scheduling and resource allocation are significant difficulties in cloud computing, frequently resulting in delayed execution times, elevated energy usage, and cost inefficiencies. Conventional scheduling techniques, like Round Robin and Genetic Algorithms, fail to dynamically adjust to variations in workload, leading to resource inefficiency and extended processing delays. This study introduces RATS-HM (Resource Allocation and job Scheduling using Hybrid Machine Learning), an innovative framework that combines Improved Cat Swarm Optimisation (CSO) with Group Optimization-based Deep Neural Networks (DNNs) for enhanced job scheduling and resource allocation. The suggested approach guarantees lowered execution time (2.5s), optimised makespan (120s), enhanced energy efficiency (85%), and increased cost savings (30%) relative to traditional models. The system constantly adjusts to changes in workload, guaranteeing efficient task execution while improving cloud infrastructure performance. Experimental findings demonstrate that RATS-HM surpasses current methodologies regarding execution speed, scalability, and efficiency, establishing it as a competitive alternative for future cloud computing environments.