A genetic load balancing algorithm to improve the QoS metrics for software defined networking for multimedia applications

With the increasing growth in the network and latest technologies by which people communicates via voice or data and modifies the radio devices easily and cost effectively. Software defined radio brings the flexibility, power and efficiency including cloud and big data, control and management of the traditional networks has raised the challenges for the development of multimedia applications. Multimedia applications require to handle the large amount of data at the servers which has increased the load on them. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the constraints of Quality of Service (QoS) and Quality of Experience (QoE) with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing. Therefore, many servers can be used with the load balancers which behave as the front end. The present paper aims to reflect impact on the efficiency of the usage of software-defined networks service in various multimedia applications. A genetic load balancing algorithm (GLBA) is proposed and is implemented on POX controller with mininet emulator in python language to compute its effectiveness and efficiency. Validation of GLBA for 100 to 600 users over server load, weighted round robin, round robin, dynamic server and LBBSRT algorithms with parameters, throughput, response time, memory and CPU utilization has proved the significance of proposed algorithm.