Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement

The optimization of resource allocation in a cloud computing environment is a problem that has been challenging due to heterogeneous tasks with varying resource requirements and different optimization objectives for execution time, energy consumption, service level agreements (SLAs) and so on. In this paper, a hybrid multi-objective optimization algorithm called AH-NSGAIII-VND is proposed for solving a multi-objective optimization problem in a cloud computing environment. The proposed algorithm integrates a global search process using a variant of a multi-objective evolutionary algorithm called Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and a local search process using a variant of a local search algorithm called Variable Neighborhood Descent (VND). The problem of resource allocation in a cloud computing environment is formulated as a multi-objective optimization problem considering makespan, energy consumption, cost, service level agreements (SLAs) and resource utilization. Researchers compared the results of the PSO, GA, and GWO optimization methods to the baseline using CloudSim-based model conditions on the changing workload scale. Experimental results indicate that cloud supply governance efficiency is enhanced with the AH-NSGAIII-VND architecture. It achieves around 11.3%, 12.8%, and 28% lower costs than the baseline NSGA-III approach while increasing overall asset utilization by over 5 percentage points. Moreover, with increasing workload, the proposed model exhibits improved convergence behavior and scalability. These results confirm that global evolutionary optimization supported by adaptive local search successfully reinstates or enhances the efficiency of resource allocation.