  The behaviour of the TCP AIMD algorithm is known to cause queue length
oscillations when congestion occurs at a router output link. Indeed, due to
these queueing variations, end-to-end applications experience large delay
jitter. Many studies have proposed efficient Active Queue Management (AQM)
mechanisms in order to reduce queue oscillations and stabilize the queue
length. These AQM are mostly improvements of the Random Early Detection (RED)
model. Unfortunately, these enhancements do not react in a similar manner for
various network conditions and are strongly sensitive to their initial setting
parameters. Although this paper proposes a solution to overcome the
difficulties of setting these parameters by using a Kohonen neural network
model, another goal of this study is to investigate whether cognitive
intelligence could be placed in the core network to solve such stability
problem. In our context, we use results from the neural network area to
demonstrate that our proposal, named Kohonen-RED (KRED), enables a stable queue
length without complex parameters setting and passive measurements.
