336 research outputs found
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A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
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A fuzzy approach for the network congestion problem
In the recent years, the unpredictable growth of the Internet has moreover pointed out the congestion problem, one of the problems that historicallyha ve affected the network. This paper deals with the design and the evaluation of a congestion control algorithm which adopts
a FuzzyCon troller. The analogyb etween Proportional Integral (PI) regulators and Fuzzycon trollers is discussed and a method to determine the scaling factors of the Fuzzycon troller is presented. It is shown that
the Fuzzycon troller outperforms the PI under traffic conditions which are different from those related to the operating point considered in the design
Design and performance evaluation of a state-space based AQM
Recent research has shown the link between congestion control in
communication networks and feedback control system. In this paper, the design
of an active queue management (AQM) which can be viewed as a controller, is
considered. Based on a state space representation of a linearized fluid flow
model of TCP, the AQM design is converted to a state feedback synthesis problem
for time delay systems. Finally, an example extracted from the literature and
simulations via a network simulator NS (under cross traffic conditions) support
our study
Managing network congestion with a Kohonen-based RED queue
The behaviour of the TCP AIMD algorithm is known to cause queue length
oscillations when congestion occurs at a router output link. Indeed, due to
these queueing variations, end-to-end applications experience large delay
jitter. Many studies have proposed efficient Active Queue Management (AQM)
mechanisms in order to reduce queue oscillations and stabilize the queue
length. These AQM are mostly improvements of the Random Early Detection (RED)
model. Unfortunately, these enhancements do not react in a similar manner for
various network conditions and are strongly sensitive to their initial setting
parameters. Although this paper proposes a solution to overcome the
difficulties of setting these parameters by using a Kohonen neural network
model, another goal of this study is to investigate whether cognitive
intelligence could be placed in the core network to solve such stability
problem. In our context, we use results from the neural network area to
demonstrate that our proposal, named Kohonen-RED (KRED), enables a stable queue
length without complex parameters setting and passive measurements.Comment: 8 pages, 9 figure
Design of Network Traffic Congestion Controller with PI AQM Based on ITAE Index
Establishing the proper values of controller parameters is the most important thing to design in active queue management (AQM) for achieving excellent performance in handling network congestion. For example, the first well known AQM, the random early detection (RED) method, has a lack of proper parameter values to perform under most the network conditions. This paper applies a Nelder-Mead simplex method based on the integral of time-weighted absolute error (ITAE) for a proportional integral (PI) controller using active queue management (AQM). A TCP flow and PI AQM system were analyzed with a control theory approach. A numerical optimization algorithm based on the ITAE index was run with Matlab/Simulink tools to find the controller parameters with PI tuned by Hollot (PI) as initial parameter input. Compared with PI and PI tuned by Ustebay (PIU) via experimental simulation in Network Simulator Version 2 (NS2) in five scenario network conditions, our proposed method was more robust. It provided stable performance to handle congestion in a dynamic network
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