A study of robust active queue management schemes for correlated traffic

Abstract

Random Early Detection detects the presence of congestion by measuring the exponentially weighted moving average (EWMA) of the queue length against two thresholds. Recently, rate-based packet marking schemes, like Adaptive Virtual Queue, have shown that they are more flexible in detecting and dealing with congestion as compared to schemes based on EWMA of queue length. In this paper we shed light on the operation of distributed local Active Queue Management (AQM) schemes by decomposing them based on their logical functions and by studying them as a combination of a measurement module and a control module. We also exploit the presence of strong correlation in network traffic in order to formulate a generalized Predictive AQM (PAQM) scheme. For this, we extend the calculation of the EWMA by including a term that represents the future traffic intensity. We compare the performance of our generalized predictive AQM scheme with a pure rate-based packet marking scheme. We study the performance of PAQM for various combinations of predictors and controllers with respect to robustness, end-to-end delay jitter, goodput and linkutilization. We present the goodput results purely with the intention that increased robustness is not at the expense of decreased goodput. Based on the simulation results, we have the following observations: (i) for proper selection of the weight of future observations, PAQM schemes achieve better delay bounds than the traditional (non-PAQM) ones. (ii) AQM schemes with stochastic based traffic prediction are usually more robust when the control is based on EWMA of queue length. Similarly, AQM schemes with traffic prediction based on finite impulse response filters are more robust when the control is based on fixed queue occupancy.(iii) More robust AQM schemes usually have higher goodput for comparable average queueing delay values, (iv) excessive dependence on predicted future arrivals always results in deterioration of performance. This sets a bound on the optimistic performance of AQM schemes even when using prediction.