Traffic engineering in software defined network using machine learning technology

Abstract

quotNow a days, traffic classification with perfection is prime need in network activities such as network security, traffic engineering, network fault detection, network usage accounting, billing and for delivering best quality-of-service (QoS) parameters of various network services. Traffic classification in network is very important in recent days due to hastily growing number of internet users. Traditional network traffic classification has failed to deliver consistent and trusted solutions due to enormous growth of communication devices and their network flows. To propose possible solution in this area of research, the integration of Software Defined Network (SDN) architecture and machine learning technology can be considered together. newlineThe SDN platform offers an opportunity to include intelligence into the networking devices. In the traditional networking system, the network devices are managed using command-line interface (CLI) and scripts. Advancement to this is SDN with semi-automation for network management. The recent version is intelligence-driven (Defined) networks, which is fully automated management of networks. In machine learning, the traffic and application patterns are learnt and refined to control the network for minimization of cost and maximizing throughput. So, in this research problem, machine learning technology will be integrated with SDN for solving traffic engineering problems.quot newline