Abstract—In this paper, a Fuzzy Logic Congestion Detection (FLCD) algorithm which synergically combines the good characteristics of traditional Active Queue Management (AQM) algorithms and fuzzy logic based AQM algorithms is proposed. The membership functions (MFs) of the FLCD algorithm are then designed automatically by using a Multi- objective Particle Swarm Optimization (MOPSO) algorithm in order to achieve optimal performance on all the major performance metrics of IP congestion control. The optimized algorithm is compared with the basic Fuzzy Logic AQM and the Random Explicit Marking (REM) algorithms. Simulation results show that the new approach provides high link utilization whilst maintaining lower jitter and packet loss. The new approach also exhibits higher fairness and stability compared to its basic variant and REM. I. INTRODUCTION HE Internet has experienced a tremendous growth over the past two decades and with that growth have come severe congestion problems. As a result, the Internet is facing increasing packet loss rates, queuing delays and jitter. Apart from wasting resources, lost packets can cause congestion collapse [1] while packet delay and jitter reduce the quality of service for interactive applications. Active Queue Management (AQM) denotes a class of algorithms designed to alleviate problems of congestion while at the same time ensuring high link utilization and fairness. Research in this area was inspired by the original Random Early Detect (RED) proposal [2]. In 1998, the Internet Engineering Task Force (IETF) recommended the deployment of RED for congestion prevention. Since this IETF recommendation, a plethora of AQM algorithms has been proposed [3], [4].These algorithms have been classified as heuristic, optimization based and control theoretic. In spite of all these developments, there has been a slow deployment of AQM algorithms in commercial circles because these algorithms perform well only for specific objectives and under specific scenarios [4]. The development of an AQM scheme that will achieve reasonably high performance for all the key AQM objectives is proving too difficult because most of the objectives are competing and non-commensurable. For instance, increasing link utilization comes at the cost of increased packet end-to- end delay because the buffer is predominantly full. Packet loss rate also increases due to buffer overflows. Clement N. Nyirenda and Dawoud S. Dawoud are with the Radio Access Technologies Centre, School of Electrical , Electronics and Computer Engineering, University of KwaZulu-Natal, Durban, South Africa(e-mail: nyirendac@ukzn.ac.za , dawoudd@ukzn.ac.za ). It has also been shown that, as capacity or delay increases, traditional AQM schemes eventually become oscillatory and prone to instability [5]. It has further been pointed out in [6] that these schemes demonstrate instability with the introduction of high bandwidth-delay links because they require a careful configuration of non-intuitive control parameters. As a result, they are non-robust to dynamic network changes. They exhibit greater delays than the target mean queuing delay with a large delay variation, and large buffer fluctuations, and consequently cannot control the router queue. It is also stressed that in actual queuing systems, the mean packet arrival rate and the service rate are frequently fuzzy i.e. they cannot be expressed in exact terms [7].Recently, the European Network for Intelligent Technologies (EUNITE) Roadmap [8] has pointed out that the application of fuzzy control techniques to the problem of congestion control in IP-based networks is suitable due to difficulties in obtaining a precise mathematical model using conventional mathematical analytical methods. Based on the fuzzy logic theory [9], several fuzzy logic AQM schemes [6], [10], [11], [12] have been developed with satisfactory results compared to the traditional approaches. The general trend in these schemes is that they use queue length, queue variation (traffic arrival rate) as inputs to the Fuzzy Logic Controller. The system output is a probability with which packets are either marked dropped or marked if Explicit Congestion Notification (ECN) is enabled. ECN is a means of explicitly notifying end-hosts of network congestion, instead of dropping packets. Although the performance of fuzzy AQM algorithms is generally better than that of traditional approaches, their major shortcoming lies in the fact that their control rules and membership functions are obtained through a manual tuning process which is based on the designer’s insight. The human factor involved in this operation makes it difficult for these algorithms to achieve optimum performance for all the key AQM objectives. The other problem is that these algorithms are generally designed with an assumption that the Internet is predominantly composed of TCP traffic, whose sources respond to congestion notification signals from routers by reducing their sending rates. Practically, the situation is not like that because apart from the non-responsive UDP traffic which accounts for (22±11) % of Internet traffic [13], the Internet is nowadays facing a growing list of non-responsive flows and anomalies such as Denial of Service (DoS) attacks and routing loops [14]. These flows do not reduce their sending rates in times of congestion as responsive TCP flows reduce their rates. Therefore, fairness diminishes exponentially as Multi-objective Particle Swarm Optimization for Fuzzy Logic Based Active Queue Management Clement N. Nyirenda, Student Member, IEEE and Dawoud S. Dawoud T 0-7803-9489-5/06/$20.00/©2006 IEEE 2006 IEEE International Conference on Fuzzy Systems Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006 2231