Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning

Task scheduling in federated multi-cloud environments is challenging owing to heterogeneous service-level agreements, decentralized resource control, and dynamic workload characteristics. The existing hybrid optimization approaches lack real-time adaptability across cloud providers also assumes centralized coordination. To work with these issues, this paper proposes Multi-Objective Non-Dominated Sorting Genetic Algorithm with Q-Learning (MO-NSGAQ). This is a hybrid multi-objective scheduling framework that tightly integrates Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Q-learning within a federated broker architecture. This proposed framework simultaneously optimizes execution cost, makespan, load imbalance, and resource utilization while adapting to inter-cloud heterogeneity. Extensive simulations are done using synthetic workloads, such as Google Cloud job traces, and IoT-based workloads. These simulations demonstrate that MO-NSGAQ reduces makespan by up to 18–32%, improves resource utilization by 10–22%, and achieves better load balance compared to existing baselines. The results confirm the proposed framework effectiveness for adaptive and scalable federated cloud scheduling.