Statistical approach for congestion control in gateway routers

Abstract

Active Queue Management (AQM) aims at minimizing queuing delay while maximizing the bottleneck link throughput. This paper describes two statistical principles that can be exploited to develop improved AQM mechanisms. The first principle indicates that the statistical characteristics of packet markings provide a performance bound of AQM in relation to the queue’s variance, which translates to a limitation of the traditional probabilistic marking. Based on the error diffusion algorithm, a simple marking strategy is proposed to reduce the queue’s variance by one order of magnitude from that attained with probabilistic drops. The second principle focuses on the relationship between the queue occupancy and the likelihood of congestion of the link. This principle reveals that the likelihood of congestion grows exponentially with queue occupancy, suggesting that drop rates ought to increase accordingly. These fundamental principles are used jointly in the so called Diffusion Early Marking (DEM) algorithm, an AQM scheme introduced in this work leading to faster reaction, higher bottleneck link utilization, lower drop rates and lower router buffer occupancy than other AQM algorithms.