PURPLE: Predictive active queue management utilizing congestion information

Abstract

Active queue management (AQM) is an attempt to find a delicate balance between two antagonistic Internet queuing requirements: first, buffer space should be maximized to accommodate the possibly huge transient bursts; second, buffer occupation should be minimum so as not to introduce unnecessary end-to-end delays. Traditional AQM mechanisms have been built on heuristics to achieve this balance, and have mostly done so quite well, but often require manual tuning or have resulted in slow convergence. In contrast, the PURPLE approach predicts the impact of its own actions on the behavior of reactive protocols and thus on the short-term future traffic without keeping pre-flow state. PURPLE allows much faster convergence of the main AQM parameters, at least towards a local optimum, thereby smoothing and minimizing both congestion feedback and queue occupancy. To improve the quality of the prediction, we also passively monitor (using lightweight operations) information pertaining to the amount of congestion elsewhere in the network, for example, as seen by flows traversing this router.