Multi-Objective Scheduling in Cloud Environments using Enterprise Development Optimizer

This paper proposes an efficient task scheduling strategy in cloud computing using the Enterprise Development Optimizer (EDO), a novel metaheuristic inspired by the strategic growth of enterprises. The approach aims to minimize makespan, energy consumption, and virtual machine (VM) failure rate, which are critical for performance, sustainability, and reliability in cloud environments. The EDO algorithm is evaluated using the CloudSim simulator on the real-world NASA Ames iPSC/860 workload and compared against established techniques ACO, PSO, and WOA. Experimental results demonstrate that EDO achieves significant improvements, with up to 28.6% lower makespan, 22.6% reduction in energy consumption, and 23.1% decrease in VM failure rate compared to existing algorithms. These results highlight EDO’s superior adaptability, scalability, and optimization capability, making it a promising solution for dynamic, multi-objective task scheduling in large-scale cloud infrastructures.