AMO Based Load Balancing Approach in Cloud Computing
Sudhanshu Mittal, Madhukar Dubey · IOSR Journal of Computer Engineering · 2017
Cloud Computing is an evolving paradigm with altering definitions, but for this research task, it's defined as a virtual infrastructure that presents shared data and communication era services.Load Balancing is any other essential issue of CC to balance the weight amongst numerous servers.It's a mechanism that distributes the additional workload dynamically and flippantly throughout all of the servers.Animal Migration Optimization (AMO) is an algorithm which is the motivation of the animal behavior.There are several animal's behavior have taken into consideration for the migration from one place to another.There are three rules which should be obeyed by all animals these are: Avoid collision with the neighbors, Move in a neighbor's similar direction and Remain close to the neighbors.In the existing work, Round Robin is used which is very time uncontrollable and this algorithm allocate Virtual Machine to the task by not taking the load information on it.The Round Robin algorithm doesn't exploit the tasks length, the capabilities of resource and priority.This makes the long completion of task and response time also higher for long tasks.In our proposed work we apply AMO technique which is the best technique to improve the load balancing in the cloud which shows in our results.In this work, firstly data center perform like population and after that AMO execute over data center.Position of animal defines as load of data center.CloudSim is utilized for the implementation CloudSim toolbox supports framework and conduct displaying of cloud framework segments, for example, data farms, virtual machines (VMs) and asset provisioning arrangements.This shows that our proposed technique is much better than the existing techniques as they performed the process in the less time and perform load balancing more efficiently