Median RED algorithm for congestion control

Abstract

The paper focuses on the queue size estimation problem in random early detection (RED) gateways. Queue size estimation plays a critical role in gateways' packet dropping/marking decisions. Conventional RED gateways use exponentially weighted moving averages (EWMA) to estimate the queue size. These IIR filters require very small weights in order to avoid nonlinear instabilities and accommodate transient congestion. Small weights, however, lead to failure of gateways to track rapid queue size depletion closely and thus causes link under utilization. We use adaptive weighted median filters for queue size estimation and study the corresponding queue dynamics. Simulation results show that the proposed algorithm provides better stability in queue dynamics, greater network power, less global synchronization, and a fairer treatment to bursty traffic than the RED algorithm.