AN ADAPTIVE ELEPHANT HERD OPTIMIZATION-BASED LOAD BALANCING ALGORITHM FOR ENERGY EFFICIENT NETWORKS

Abstract

In recent years, cloud computing has emerged as a major player in the field of information knowledge. Cloud companies are building data centers (DCs) around the world to fulfill the increased demand for computing and storage resources. When it comes to cloud computing, load balancing refers to the practice of spreading workloads and computational properties among multiple nodes in a cluster. Organization can manage their workloads and applications better by distributing resources among PCs, networks, and servers. The AEHO (Adaptive Elephant Herd Optimization) algorithm was used in this study to design a load-balancing system. The elephant population is alienated into clans, with each point on the elephant's trunk denoting a different type of solution. The best solution can be found in a family with a matriarch at the helm. For making and responding to tasks, the suggested AEHO algorithm was found to have an effective average load. Compared to other approaches, the simulation consequences established that the projected AEHO model performed effectively. With an average response period of 13.58 milliseconds, a turnaround time of 21.09 milliseconds, reliability of 87%, and a throughput of 99 percent with a 45-millisecond makespan for minor operations, the simulation consequences show that the AEHO procedure outperformed all other compared approaches.