Multi-Rumen Anti-Grazing approach of load balancing in cloud network

Abstract

In this globalized world with the advancement of technology the use of computation and simulation gradually increases. To fulfill the increased user demand cloud network provides its ubiquitous service in rent basis. The augmented ultimatum of cloud service increase loads on virtual machines and fallouts load imbalance in cloud system. There are many challenges associated with the cloud system, load balancing is one of them. Proper resource utilization and minimization of makespan is the basic motive of load balancing. This paper describes a multi-datacenter load adjustment technique called Multi-Rumen Anti-Grazing algorithm for assigning tasks to virtual machines of different datacenters. Our proposed mechanism is a static load balancing strategy that concerns about minimization of makespan and it gives better result than the existing ones. The simulation is carried out with different randomly generated datasets and result is compared with static Min–Min and ELBMM algorithm. In each time the proposed multi-datacenter method gives better performance and makespan as compare to the traditional intra datacenter Min–Min and ELBMM technique.