EFBOA: Optimizing Resource Utilization in Cloud Environment using Meta-Heuristic Scheduling Algorithm

The critical aspect of effective Task Scheduling (TS) in Cloud Computing (CC) scenarios optimizes resource utilization and execution time while minimizing energy expenses. This paper proposes a novel Electric Fish Butterfly Optimization Algorithm (EFBOA) that integrates the adaptive movement strategy of Electric Fish Optimization (EFO) with the foraging behavior of Butterfly Optimization Algorithm (BOA) to enhance local and global search capabilities. The EFBOA improves scheduling efficiency by dynamically adjusting task allocation based on workload variations and resource availability, ensuring a balance between exploitation and exploration. Experimental evaluations demonstrate that EFBOA outperforms conventional algorithms, including BOA, EAEFA, and AGWO in terms of makespan, energy consumption, resource utilization, and throughput improvement of 16.2%, 18.4%, 14.9%, and 19.6% respectively. The proposed approach significantly reduces computational overhead while enhancing the Quality of Service (QoS) in cloud environments. The results validate EFBOA’s superiority in achieving cost-effective and energy-aware scheduling, making it a robust solution for modern cloud infrastructures.