Different techniques for congestion avoidance algorithms in bottleneck networks

Abstract

In this thesis, we systematically examine the ECN (Explicit Congestion Notification) mechanism to be applied to some AQM (Active Queue Management) techniques developed in recent years, namely, RED (Random Early Detection), BLUE, ARED (Adaptive RED) and PI-RED (Proportional and Integral RED). We use packet marking as the indication of network congestion, instead of the strategy of dropping packet currently adopted by these AQM algorithms. The performance comparison of ECN-AQM with AQM has been conducted in not only a single bottleneck network but also a multi-bottleneck network environment. The simulation results have shown that ECN-AQM algorithms can greatly decrease unnecessary packet loss, one of the main shortcomings of RED and some other AQM algorithms. They can also reduce average queue size in most cases, but the queue size oscillation problem remains. We therefore investigate the application of the head dropping policy to AQM techniques, as a simple solution to the problem of queue oscillation, another shortcoming of RED and its variants. With this method, instead of tail dropping, which is currently used by RED and many other AQM schemes, the TCP source can be informed of the congestion occurring in the bottleneck router earlier by getting rid of time to wait through the queuing delay. We have compared DH-RED (Drop Head RED) and DH-BLUE (Drop Head BLUE) with the current RED and BLUE in both of the single bottleneck and the multi-bottleneck networks. We found the performance of queue size stability can be greatly improved by DH-RED and DH-BLUE.