Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier

Abstract

Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. Intelligent Genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized Intelligent Genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized Intelligent Genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized Intelligent Genetic Load Balancer system performance with existing simulations results and compared the results.