PURPLE: Predictive Active Queue Management Utilizing Congestion Information

Active Queue Management (AQM) tries 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 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 per-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.

By: Roman Pletka, Marcel Waldvogel and Soenke Mannal

Published in: RZ3489 in 2003


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