ECO-FISH: An Enhanced Cloud Task Scheduling Using Opposition-Based Artificial Fish Swarm Algorithm

The rapid expansion of cloud computing has increased the complexity of task scheduling and resource management across heterogeneous and dynamic environments. Conventional heuristic methods often converge prematurely and lead to imbalanced virtual machine (VM) utilization. To address these challenges, this study introduces ECO-FISH, a hybrid Opposition-Based Artificial Fish Swarm Algorithm (AFSA) designed for efficient cloud task scheduling. AFSA is chosen for its swarm intelligence behaviors—prey, follow, and swarm—that enable effective local exploration with low computational cost, while Opposition-Based Learning (OBL) is integrated to strengthen global exploration by evaluating opposite task–VM mappings, helping the algorithm escape local optima and maintain population diversity. This synergy improves the balance between exploration and exploitation while retaining algorithmic simplicity. The proposed ECO-FISH is implemented in CloudSim and benchmarked against GA, PSO, and the baseline AFSA using three workload distributions: uniform, normal, and stratified. Experimental results show that AFSA alone reduces makespan by 28–45%, increases throughput by 34–84.9%, and improves utilization by 44.12– 64.59% compared to GA. The OBL enhancement in ECO-FISH provides additional gains of up to 1.6%, showing the most significant improvement under heterogeneous, stratified workloads with high variance. Overall, AFSA performs well on uniform datasets, while ECO-FISH (AFSA + OBL) offers superior adaptability and stability in variable cloud environments.