Performance Analysis of AQM Scheme Using Factorial Design Framework

The active queue management (AQM) is a router assisted congestion control technique which improves the performance of the networks. The AQM algorithm handles the congestion by dropping the packets from a congested router where dropping probability is computed with the help of average queue size, loss-rate, queuing delay, or other parameters. A novel approach based on design of experiments is proposed to study the performance measures related to several AQM schemes viz., random early detection (RED), random exponential marking, modified RED, adaptive RED, stabilized RED, three-section RED, and AQM with random dropping. The impact of several input factors on the performance measures viz., throughput, queuing delay, loss-rate is investigated by using the factorial design where it is used to find the interaction of input factors. The relative changes in the output responses on account of changes in input factors or variables are evaluated. Sensitivity analysis is carried out by computing the weighted sum of relative changes of response variable with respect to input factors for each AQM scheme. Based on the sensitivity analysis we find that a new AQM with random dropping is most robust while in contrast three-section RED is least robust.