FLRED: an efficient fuzzy logic based network congestion control method

Abstract

The number of applications running over computer networks has been increasing tremendously, which increased the number of packets running over the network as well leading to resource contention, which ultimately results in congestion. Congestion increases both delay and packet loss while reducing bandwidth utilization and degrading network performance. Network congestion can be controlled by several methods, such as random early detection (RED), which is the most well-known and widely used method to alleviate problems caused by congestion. However, RED and its variants suffer from linearity and parametrization problems. In this paper, we proposed a new method called fuzzy logic RED (FLRED), which extends RED by integrating fuzzy logic to overcome these problems. The proposed FLRED method relies on the average queue length (aql) and the speculated delay (DSpec) to predict and avoid congestion at an early stage. A discrete-time queue model is used to simulate and evaluate FLRED. The results showed that FLRED outperformed both RED and effective RED (ERED) by decreasing both delay and packet loss under heavy congestion. Compared with ERED and RED, FLRED decreased the delay by up to 1.5 and 4.5% and reduced packet loss by up to 6 and 30%, respectively, under heavy congestion. These findings suggest that FLRED is a promising congestion method that can save network resources and improve overall performance.