Controlling Internet queue dynamics using recursively identified models

Data traffic on the Internet of today is controlled by a non-linear controller (TCP) at each sender node, which increases packet transmission rate each time an acknowledgment is received in due time, and decreases otherwise. The routers may co-operate with TCP by deliberately dropping packets, so called early drops. The idea is to decrease packet arrival rate before the queue becomes full and hard drops of packets are necessary. State of the art is to compute the probability of an early drop as a static function of the (filtered) queue length. We propose to use an auto-regressive model for the oscillative behavior of the queue length that can be observed in practice. With this model, the queue length can be predicted and a dynamic algorithm for computing the early drop probability can be used. We suggest a very simple modification of existing algorithms, where a short-time prediction is used instead of the current queue value, and demonstrate using ns-2 simulations that the overall throughput increases.