Enhancing cloud performance through grey wolf optimization: A robust approach to load balancing

Much progress in computing has resulted from the advent of cloud computing. End users may reap the benefits of a plethora of cloud technologies. Services are accessible through online login only. Load balancing is the cornerstone problem in cloud computing that has stumped researchers. Users are happier and systems are more productive when load balancing is used to distribute tasks evenly across all available CPU cores. Moreover, it would be difficult to maintain a load balance across resources since resources are often spread in a dispersed fashion. By using a met heuristics approach, several load-balancing techniques have sought to optimize system performance. In this research, we apply the Optimization of gray wolves (OGW) technique to balance loads reliably among all available resources. In the first step, the OGW algorithm looks for idle or busy nodes, and then it attempts to determine the threshold and fitness function for each of these nodes. Simulation findings in CloudSim confirmed that the suggested approach yields superior outcomes in terms of cost and reaction time.