Makespan Optimization for Resource Scheduling using NGACO Algorithm

Resource scheduling (RS) for makespan minimization is essential in maximizing the efficiency of cloud computing (CC) systems. Makespan is the total completion time for a set of computational tasks. Its minimization is essential in maximizing system throughput and resource utilization, especially in dynamic and large-scale cloud systems. Scheduling in CC, however, is extremely challenging due to resource heterogeneity, dynamic task loads, and uncertain usage patterns. Classical algorithms are expected to fail in handling such complexity. In handling such complexity, this research proposes a hybrid metaheuristic algorithm called New Genetic Ant Colony Optimization (NGACO). The NGACO algorithm combines the exploration capability of Genetic Algorithms (GA) and the exploitation capability of Ant Colony Optimization (ACO) to balance and efficiently allocate tasks to resources. Through the combination of genetic diversity and pheromone-based learning, NGACO effectively minimizes makespan and maintains load balance among virtual machines. Experimental evaluations confirm that NGACO outperforms existing scheduling algorithms, offering a promising solution to cloud-based resource optimization