A New Method to Increase Load Balance in a Cloud Computing Environment Based on an Improved Genetic Algorithm

With the high potential that cloud computing has for remote data storage and processing, Cloud computing has introduced a new computing technology for storing and processing data. In cloud service environments, virtual machines from multiple organizations are placed on a physical server, which increase the efficiency of virtualization. In such a scalability infrastructure, we have some bottlenecks and congestion in our cloud without managing traffic. So, we need the solutions to balance the load and distribute tasks among other processing servers. We present an advanced solution based on optimized genetic algorithm for scheduling and balancing load in cloud infrastructure in this paper. We add some parameters to the algorithm to check resource status before scheduling. In fact, the proposed technique prevents overload or underload on the servers by optimally assigning tasks to processing servers and the tasks of high-load and congested servers are transferred to another server using live migration of the virtual machine, in order to increase the load balance of the cloud infrastructure At the end, the proposed solution is evaluated through the Cloudsim simulator and by testing the volume of more than a thousand virtual machines on the Plant Lab data. The results of the simulation results show that the proposed solution has been able to violate the service level agreement compared to the AMUT  and EQVS methods on average 46%, the energy consumption criterion on average by 18 % and averages number of virtual machine migrations improved by 24%.