On exploiting traffic predictability in active queue management
Yuan Gao, Guanghui He, Jennifer C. Hou · 2003
Abstract — Analytical and empirical studies have shown that selfsimilar traffic can have detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to better design resource control algorithms. In this paper, we explore the issue of exploiting traffic predictability to enhance the performance of active queue management (AQM). We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to accurately predict the future traffic. To this end, we design and introduce two non-model-based traffic predictors: LMMSE and simple. We then figure in, with the objective of stabling the instantaneous queue length, the prediction results in the calculation of the packet dropping probability. The resulting scheme is termed as predictive AQM (PAQM).