A fuzzy logic-based AQM for real-time traffic over internet

Autonomic networking has been proposed as an approach to reduce cost and complexity of managing communication functions. An autonomic system is self-configuring, self-optimizing, self-healing and self-protecting. Such a system requires the minimum of administration, primarily involving policy-level management and AI-cognitive models. On the other hand, numerous Active Queue Management (AQM) algorithms have been proposed in the literature to address the problem of congestion in the Internet. Their performance is highly dependent on parameters' setting and tuning. Besides that, most of the AQM algorithms focus on throughput optimization and fail to provide bounded transmission delay while providing high link utilization to popular TCP-based radio/video streaming applications. Tackling the aforementioned concerns, in this paper we propose and evaluate a novel self-configuring AQM algorithm based on fuzzy logic. The proposed approach simplifies significantly the deployment and management of such complex QoS control mechanisms in the Internet providing at the same time a good tradeoff between link utilization and queuing latency. The introduced algorithm is compared with the most efficient adaptive AQM algorithms proposed to date such as ARED, REM, BLUE, PID and LRED. The performance analysis demonstrates that the proposed ''Fast and Autonomic Fuzzy Controller'' (FAFC): (1) minimizes queue fluctuation, (2) optimizes the throughput regardless of the traffic load variation and the presence of unresponsive UDP/RTP based voice and video communications, and (3) suggests the best compromise between link utilization and queuing delay.

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