Genetic Approach based Optimized Load Balancing in Cloud Computing: A Performance Perspective

Cloud computing is a technology that facilitates information interchange and provides a large number of resources to cloud users. It is fast growing; a large number of customers are being drawn to cloud administrations in search of greater delight. In this field, balancing the load has turned into a really intriguing examination subject. For powerfully scattering load among various cloud nodes in the framework, a superior load balancing algorithm will boost performance and resource usage. For cloud workload, successful VM migration using effective genetic algorithm approach brings about ideal load management. The work in this paper puts forward an advancement in existing genetic algorithm i.e., IGA (Improved Genetic Algorithm) for effective VM migration and progressive load handling in cloud domain. The achieved results shows that proposed IGA method notably reduces the time taken for the load balancing procedure as compared against the results of the existing used genetic algorithm (GA). Furthermore, the proposed approach minimizes virtual machine (VM) migrations delay while simultaneously reducing energy usage and the number of migrations. As a result, the proposed solution will eliminate VM downtime, improve Quality of Service, and save costs for cloud clients.