Adaptive Filtering Queueing for Improving Fairness


In this paper, we propose a scalable and efficient Active Queue Management (AQM) scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ). First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ), Core-Stateless Fair Queueing (CSFQ), CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe) and First-In First-Out (FIFO) schemes under a variety of traffic conditions.

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@inproceedings{Yang2015AdaptiveFQ, title={Adaptive Filtering Queueing for Improving Fairness}, author={Jui-Pin Yang and Christos V. Verikoukis}, year={2015} }