An Ensemble of Bacterial Foraging, Genetic, Ant Colony and ParticleSwarm Approach EB-GAP: A Load Balancing Approach in CloudComputing
Bhupesh Kumar Dewangan, Tanupriya Choudhury, Anurag Jain, Ram Narayan Shukla
Abstract
Background: In the cloud environment, satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumer and service provider. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objective: The research aim is to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, authors have proposed a new load-balancing algorithm named eB-GAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant-Colony algorithm. Based on the availability of resources and load on a virtual machine, a fitness value is assigned to all virtual machines. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment.