Background:
Active Queue Management (AQM) is a TCP congestion avoidance approach
that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection
(RED) is an AQM strategy that keeps history of queue dynamics by estimating an average
queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter
configuration in RED is problematic and the performance of the whole network could be reduced
due to wrong setup of these parameters. Drop probability is another parameter calculated by
RED to tune the drop rate with the aggressiveness of arriving packets.
Objective:
In this article, we propose an enhancement to the drop probability calculation to increase
the performance of RED.
Methods:
This article studies the drop rate when the average queue size is at the midpoint between
the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop
rate in this area. Hence, we call this strategy as the Half-Way RED (HRED).
Results:
Our strategy is tested using the NS2 simulator and compared with some queue management
strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization
and packet drop rate.
Conclusion:
Each performance parameter has been plotted in a separate figure; then the robustness
of each strategy has been evaluated against these parameters. The results suggest that this function
has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed
the strategies included in this article in terms of throughput, link utilization and packet loss
rate.
Channel quality aware active queue management in cellular networks