Multi-objective load balancing based on adaptive osprey optimization algorithm

With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load balancing approach based on adaptive osprey optimization algorithm (AO²) within computing environments is proposed. Our proposed method aims to simultaneously optimize multiple objectives, such as minimizing energy consumption, cost and time, through the application of advanced optimization algorithms. The proposed system initially predicts the load of each virtual machine (VMs). After the prediction process, the tasks are assigned to the VMs. The research involves a comprehensive evaluation comparing the proposed approach with existing load balancing techniques, showcasing its effectiveness in achieving superior results. The findings demonstrate the potential of the multi-objective optimization algorithm to enhance load balancing efficiency in diverse computing scenarios, providing a valuable contribution to the field of distributed systems and resource management.