RED gateway congestion control using median queue size estimates

This paper focuses on the queue size estimation problem in random early detection (RED) gateways. Queue size estimation plays a fundamental role in the congestion control dynamics of RED, as it determines gateways' awareness of network congestion, which in turn determines the packet dropping/marking decision. Conventional RED gateways use exponentially weighted moving averages (EWMA) to estimate the queue size. These infinite impulse response (IIR) filters require very small EWMA weights in order to effectively avoid nonlinear instabilities in RED and to filter out bursty increases in the queue size. While small EWMA weights enable gateways to accommodate transient congestion, they also lead to gateways' failure to closely track rapid queue size depletion and thus causes link under utilization. We investigate the use of simple nonlinear queue size estimators. In particular, we study the congestion control dynamics of a network where adaptive weighted median filters are used for queue size estimation by the gateways. Analytical results for the expected queue size in the steady state are derived. Under this new queue size estimation framework, design guidelines for the remaining RED parameters are provided. Simulation results show that the proposed algorithm provides greater network power, better prevention of global synchronization, and a fairer treatment to bursty traffic than the RED algorithm does.