BfOA: A Battlefield Inspired Metaheuristic for Efficient Task Scheduling in Cloud Environments
Nikitha Masuna, S Reddy, B Murthujavali, Newton Gattu, Nandikante Shravani, Santhosh Kumar Medishetti
Abstract
Efficient Task Scheduling (TS) in Cloud Computing (CC) plays a critical role in optimizing resource utilization, minimizing energy consumption, and improving system throughput. This paper proposes a novel task scheduling approach based on the Battlefield Optimization Algorithm (BfOA) to enhance performance in a cloud environment. Inspired by real-world battlefield strategies, BfOA incorporates adaptive decision-making mechanisms to dynamically allocate tasks across virtual machines (VMs). The proposed method is implemented and evaluated on Google Cloud using the CloudSim simulator. Performance is assessed in terms of three key parameters: makespan, energy consumption, and throughput. Comparative analysis is conducted against three well-established metaheuristic algorithms such as ABC, PSO, and WOA. Experimental results demonstrate that BfOA significantly outperforms the baseline models, achieving an average improvement of up to 18.7% in makespan reduction, 21.4% in energy efficiency, and 15.9% in throughput enhancement. The BfOA approach not only balances the computational workload efficiently but also adapts to varying cloud conditions, making it highly effective for real-time and large-scale task scheduling scenarios. This work highlights the potential of battlefield-inspired strategies in addressing the complex multi-objective optimization challenges in cloud computing environments.