Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) an effective method for CBS problems, still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior solutions. In order remove WOA limitations, hybrid oppositional differential evolution-enabled (called h-DEWOA) approach introduced tackle problems minimize workload makespan energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), evolution (DE), fitness-based balancing mechanism into the standard method, resulting enhanced exploration, faster convergence, throughout algorithm execution. Besides this, efficient allocation heuristic added improve resource assignment. CEA-Curie HPC2N real workloads are used performance evaluation of algorithms using CloudSim simulator. Two series experiments have been conducted comparison: one with WOA-based heuristics another non-WOA-based metaheuristics. Experimental results first reveal that range 5.79–13.38% (for workloads), 5.03–13.80% consumption 3.21–14.70% workloads) 10.84–19.30% Similarly, also resulted significant comparison recent state-of-the-art metaheuristics second experiments. Statistical tests box plots revealed robustness algorithm.