Deep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks

Abstract

Software-defined Networking (SDN) provides an easy way to monitor network and traffic conditions by employing software-based controllers to communicate with the hardware directly. It provides helpful information that enables efficient routing decisions. This research study attempted to use deep learning techniques—Long Short-term Memory, Bidirectional Long Short-term Memory, and Gated Recurrent Unit—to predict network traffic to allow the controller to early detect congestion. The traffic flow in a network link that will likely be congested will be rerouted to a new path with the largest available bandwidth. Various scenarios were simulated to evaluate our deep learning-based SDN controller (Ryu controller platform). The results show that our proposed deep learning-based SDN controller outperformed the traditional load balancing technique.