A Study on Performance Improvement of Adaptive AQM Using the Variation of Queue Length

큐 변화량을 이용한 적응식 AQM 성능 향상에 관한 연구

Random Early Detection (RED), one of the most well-known Active Queue Management (AQM), has been designed to substitute Tail Drop and is nowadays widely implemented in commercially available routers. RED algorithm provides high throughput and low delay as well as a solution of global synchronization. However RED is sensitive to parameters setting, so the performance of RED, significantly depends on the fixed parameters. To solve this problem, the Adaptive RED (ARED) algorithm is suggested by S. Floyd. But, ARED also uses fixed parameters like target-queue length; it is hard to respond to bursty traffic actively. In this paper, we proposed AQM algorithm based on the variation of current queue length in order to improve adaptability about burst traffic. We measured performance of proposed algorithm through a throughput, marking-drop rate and bias phenomenon. In experimentation, we raised a packet throughput as reduced packet drop rate, and we confirmed to reduce a bias phenomenon about bursty traffic.