Abstract
Active queue management (AQM) mechanism is a powerful method, which aims to assist the TCP congestion control and to improve the trade-off between queuing delay and link utilization. Traditional price-based AQM algorithms suffer from sluggish response, poor robustness, and lack adequate adaptability against dynamic traffics. To improve AQM performance, this paper introduces artificial intelligence methods to design a sophisticated AQM algorithm. In particular, a fuzzy neuron price is developed for congestion detection. Hebbian learning rule and fuzzy logic theory are employed to configure the control parameters automatically for better adaptability and robustness. Simulation results demonstrate that our proposed scheme is stable, responsive and performs robustly against time-varying network dynamics. It is superior to other peer AQM algorithms in various performance indicators, such as stability and jitter of queue length as well as packet loss.
Original language | English |
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Title of host publication | 2011 IEEE Global Telecommunications Conference, GLOBECOM 2011 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Print) | 9781424492688 |
DOIs | |
Publication status | Published - 2011 |
Externally published | Yes |
Event | IEEE Global Telecommunications Conference 2011 - Houston, United States of America Duration: 5 Dec 2011 → 9 Dec 2011 https://ieeexplore.ieee.org/xpl/conhome/6132211/proceeding (Proceedings) |
Conference
Conference | IEEE Global Telecommunications Conference 2011 |
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Abbreviated title | IEEE GLOBECOM 2011 |
Country/Territory | United States of America |
City | Houston |
Period | 5/12/11 → 9/12/11 |
Internet address |