Artificial Bee Colony Based Ensemble Genetic Algorithm For Resourse Allocation And Load Balancing On Cloud Computing

  • P. Jayasri Archana Devi, M. Jayanthi , M. Pavithra Rao, S. Selvakumaran, P. S. Satheesh. B. Gobinathan

Abstract

 A cloud is a kind of concurrent and distributed processes composed of interconnected as well as virtualized computers. By utilizing the internet services, it is possible for one to possess the level expert services to fulfill their requirements; customization of application and from anywhere can access the Cloud Services. Cloud computing plays significant role in our society, because it provides wide range of applications by making use of Internet. Cloud Computing gives services to its users based upon “pay as you go” model. However, providing high availability to cloud resources to its users is still an open area of research. Cloud service provider utilizes workflow scheduling techniques to minimize the waiting time of users. In this project Genetic Algorithm based Artificial Bee Colony (GA-ABC) workflow scheduling technique is proposed. Live migration is also done to balance the load between available virtual machines. ABC is good foraging behavior associated with honey bee swarm Inside the ABC style. The colony involves several teams of bees: Employees-bee, Onlookers-bees, and Scouts. ABC as an optimization tool, provides a population-based search procedure in which individuals called foods positions are modified by the artificial bees with time and the bee’s aim is to discover the places of food sources with high nectar amount and finally the one with the highest nectar (most optimal solution) combined with the power of evolutionary computation using Genetic Algorithms. Majority of scheduling techniques used by cloud service providers to minimize the waiting time of users are NP-Complete in nature. Therefore, the project utilizes artificial bee colony with GA based workflow scheduling technique to outperform and optimize live mitigation in cloud data centers in terms of execution cost, efficiency, utilization, energy consumption, and speedup.

Published
2020-04-24
How to Cite
P. Jayasri Archana Devi, M. Jayanthi , M. Pavithra Rao, S. Selvakumaran, P. S. Satheesh. B. Gobinathan. (2020). Artificial Bee Colony Based Ensemble Genetic Algorithm For Resourse Allocation And Load Balancing On Cloud Computing. International Journal of Control and Automation, 13(03), 409-416. Retrieved from http://sersc.org/journals/index.php/IJCA/article/view/38112