Cloud computing is one of the rapid-growing techniques that support computation by providing software applications and the hardware infrastructure over the Internet in a remote location. It allows us to run an application efficiently in the virtualized environment at a reduced cost. In Cloud computation, the utilization of Cloud resources should take place effectively since it relates to an agreement between the user and the service provider, namely, Service-level agreement (SLA). The SLA violations occur while the resources get overloaded; that is, the utilization of the resources exceeds its capacity. The overloaded resources significantly increase the makespan, the CPU utilization rate that fails the resources.
On the other hand, under-loaded resources reduce the makespan effectiveness; however, it increases power consumption. Hence, the effective utilization of the resource takes place within the balanced state is possible using the load-balancing technique. The load-balancing is obtained using various techniques, for instance, migration, resource allocation, and task scheduling. Various researchers effectively manage the load in specific resources using several systematic load-balancing algorithms. In some of the research, they suggest an algorithm for estimating the completion time of the task in order to balance the workload in certain resources of the data center. This load balancing algorithm effectively minimizes resource utilization response time and avoids VM migration. Frequently, the existing algorithm faces difficulties in achieving effective load-balancing since they cannot perform the load-balancing within the minimized response time and execution time.
Furthermore, the performance of the existing algorithm gets degraded when improper resource utilization fault tolerance takes place. Notably, the resources in the data center face significant challenges in balancing the load while executing the task based on its priority. Most of the existing algorithm tries to minimize the demerits mentioned above using the dynamic scheduling algorithm. However, the selection of the load, transferring the load, and selection of the target resources tend to increase the run-time overhead. Therefore, many researchers attempt to minimize the demerits in the existing static scheduling since these algorithms try to minimize the overall time consumption. Thus, the load distribution across the multiple resources in the data center effectively minimizes the makespan, cost, and power consumption and achieves higher resource utilization.