Adaptive Mean Queue Size and Its Rate of Change: Queue Management with Random Dropping

TL;DR

This paper proposes an adaptive queue management algorithm that uses both the average queue size and its rate of change to improve network performance metrics like throughput and delay.

Contribution

The novel AQMRD algorithm incorporates the rate of change of queue size and adaptive thresholds, enhancing performance over traditional RED schemes.

Findings

Improved throughput and utilization

Reduced average queue size and delay

More stable queue management under heavy load

Abstract

The Random early detection (RED) active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping (AQMRD) algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same. Introducing an adaptively changing threshold level that falls in between lower and upper thresholds, our algorithm demonstrates that these additional features significantly improve the system performance in terms of throughput, average queue size, utilization and queuing delay in relation to the existing AQM algorithms.