DyRED: An Enhanced Random Early Detection Based on a new Adaptive Congestion Control

Abstract

Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.