Decentralized Load Balancer in Cloud Environment by using Multi Attribute Decision Making Policy

In the cloud environment, load balancing is done by choosing the appropriate VM (Virtual Machine) between existing VMs to execute the given task. Choosing the appropriate VM to do any task, is function of various parameters. In this paper, we choose the most appropriate VM to execute the task by considering each of criteria. Therefore, we consider this selection as a Multi Attribute Decision Making (MADM) problem. At first, we model the problem in terms of effective parameters in performance by considering the basic goals of the load balancing. Then, we solve the model by using the non-determenestic taxonomy method, which is one of the most widely used method for MADM problems. In this method which is called TLB, desirability of each VM is calculated according to the weights assigned to the criteria and the level of importance of each criterion for the user which is determined based on entropy method.Finally, the best VM is choosen based on its assigned value. To study the performance of the proposed approach, extensive simulations is carried out in CloudSim simulator which show that the proposed method has better performance compared to FIFO, DLB and WRR method.