Artificial Neural Network Based Load Balancing On Software Defined Networking

Cloud computing is an emerging area where all the network services are provided in the form of pay per use. Utilizing the available resources efficiently is one of the challenge in the data center network. Cloud resources are given as virtual machine to the end users. Sometimes, Virtual Machines (VMs) are overloaded or underloaded. The overloaded VMs degrade the performance of applications and underloaded VMs lead to inefficient resource utilization. To effectively utilize the available resources, we have proposed Dynamic Agent-Based'Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN). This load balancing algorithm uses the global visibility of SDN to efficiently migrate virtual machines in the data center network. The results show that our SDN load balancing approach improves the overall network efficiency and works well on data migration.The proposed algorithm helps to increase the processing speed and predict the loaded VM in heavy load conditions and optimizes the resource utilization. The migration process results are compared with Multi-Path TCP (MPTCP) and Heuristic algorithm (HA) and it is seen that our proposed algorithm takes less migration time compared to MPTCP and HA.