An Efficient Load Balancing Using Genetic Algorithm in Cloud Computing

Distributing jobs to servers is an essential factor affecting the performance of a cluster server. Load balancing is a method to distribute jobs to a group of computers to optimize resource utilization, throughput, or response time. A large number of requests increase the load on the cloud. The load has to be balanced to distribute the dynamic workload throughout a couple of nodes to make sure that no single resource is both crushed or underutilized. Machine learning is used to automate the decision of the load balancing algorithm, and machine learning is considered to provide advantages in load balancing. Various strategies can be used inside the Genetic Algorithm (GA) approach selection procedure on the problem. Selection techniques used depend on the problem to be solved. This paper proposes GA with Tournament Selection and Roulette Wheel Selection. These two selection techniques have been proposed as load balancing techniques for cloud computing to find global optimal processors for tasks in the cloud. The result of this research is that the GA using Tournament Selection is better than the GA using the Roulette Wheel Selection.