A prediction-based active queue management for TCP networks

The emergence of new kinds of applications and technologies (e.g., data-intensive applications, server virtualization) has led to a better utilization of the network resources. However, it has also led to more bandwidth consumption and more congestion especially inside data center networks. Thus, researchers are focusing again on TCP and Active Queue Management (AQM) mechanisms in order to better control congestion and to cope with application requirements in terms of end-to-end delay [1], [2], [3]. Recently, we proposed a new AQM mechanism (called α_SNFAQM) that uses traffic prediction to accurately detect future congestion and to proactively act upon it [4]. In this paper, we develop an analytical model to assess the effect of α_SNFAQM on TCP. The study proves that this AQM is efficient enough to stabilize queue size in routers/switches, and thereby allowing to control end-to-end packet delay. These results have been also validated by simulations for a topology with multiple bottleneck links. They show that α_SNFAQM outperforms other AQM schemes like RED, PAQM and APACE in stabilizing instantaneous queue length, while keeping a high utilization of the links and the same packet loss rate.