Intelligent Federated Cloud Scheduling with Transfer Learning and Hybrid Reinforcement-Meta-Heuristic Optimization

Cloud computing has transformed the sharing of the resources and service delivery but effective scheduling of tasks across federated multi-clouds environment is a difficult task because of the heterogeneous nature of resources, dynamic nature of workloads, and SLA constraints. Current solutions tend to be either local or global at the cost of other. This paper presents a proposal of an Intelligent Federated Cloud Scheduling model that combines transfer learning based on predictive workload modeling and a hybrid reinforcement-meta-heuristic optimization of dynamic task allocation. The framework was tested on the Google Cluster Trace dataset, where it was compared to five state-of-the-art algorithms, with a decrease in makespan of 15 percent, energy usage reduced by 12 percent, SLA violation rate of 2.5 percent and resource utilization efficiency of 92 percent. The findings indicate the scalability, strength and viability of the framework to process dynamic and heterogeneous workloads. This paper presents the promise of predictive AI with hybrid optimization on intelligent cloud management, which provides a general understanding of practical research on automated, energy-efficient, and SLA-compliant federated cloud scheduling.