An Efficient Load Balancing HBLBACO Approach Using Hybrid BAT and LBACO Algorithm in Cloud Environment

Cloud computing has emerged as a crucial paradigm for delivering scalable and efficient services to many users. Load balancing in cloud environments presents several challenges, such as optimizing makespan, degree of imbalance, standard deviation, enhancing system performance and processing speed, and ensuring a reliable cloud infrastructure. These challenges are exacerbated by dynamic and unpredictable workloads, which can lead to uneven distribution of tasks and underutilization or overloading of resources. To address the challenges proposed by dynamic and unpredictable workloads, various load-balancing algorithms have been proposed. This work presents a novel approach called the HBLBACO (Hybrid BAT and LBACO) algorithm to balance the load on cloud, which combines the strengths of the Bat algorithm (BA) and the Load Balancing Ant Colony Optimization (LBACO) algorithm that is local optima and global optima respectively to achieve improved load distribution in cloud environments. To analyse the proposed algorithm, extensive experiments were conducted using CloudSim simulation environments. The experimental results demonstrate that the HBLBACO algorithm reduces makespan, degree of imbalance, standard deviation and maximized processing speed. It effectively adapts to dynamic workload changes and achieves a more balanced distribution of tasks across VMs, leading to improved system performance. The results confirm that the proposed approach outperforms 8%, 68%, 71%, 81% then LBACO, 2%, 21%, 43%, 96% then ACO and 53% ,96%, 98% then PSO algorithm in terms of makespan, degree of imbalance, standard deviation and processing speed respectively.