MLPRS: A Machine Learning-Based Proactive Re-Routing Scheme for flow classification and priority assignment

Abstract

In modern networking scenarios, the transmission of numerous burst packets is a common requirement. In this context, Software Defined Networks (SDNs) have been demonstrated to be an effective solution with a programmable centralized controller for managing complex networks. This article aims to propose a Machine Learning-based Proactive Re-routing Scheme (MLPRS) that aims to enhance the Quality of Service (QoS) by dynamic load balancing in real-time network topology. The proposed scheme applies machine learning (ML) techniques to classify applications and assign flow priorities based on their type. The betweenness centrality algorithm is used to identify the importance of each link in the network, and the links are ordered in the betweenness set. The proposed method monitors the load on each link and if the critically important links are overloaded, then the flow is routed in real-time to avoid congestion. The idea of MLPRS has been simulated in Mininet with an OpenDaylight controller, and the results show that it effectively improves QoS and network performance. The MLPRS approach contributes to the ongoing efforts in developing intelligent and dynamic load-balancing techniques using machine learning algorithms. The performance of MLPRS has been improved by 3.7 % and 3.6 % in terms of Throughput and Bandwidth respectively when compared with the default SDN.