FAFC: fast adaptive fuzzy AQM controller for TCP/IP networks

Aoul, Yassine Hadjadj; Nafaa, Abdelhamid; Negru, Daniel; Mehaoua, Ahmed

doi:10.1109/glocom.2004.1378200

Cited by 23 publications

(12 citation statements)

References 13 publications

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“…Its capacity is 10 Mbps (2621 packets/s, default packet size is 500 bytes), and delay is 20ms. Each queue may contain up to 400 packets [21]. However, the most performing queue length is of 80 packets.…”

Section: Simulation Modelmentioning

confidence: 99%

Fuzzy-based PID active queue manager for TCP/IP networks

Mohammadi

Pour

Jafari

et al. 2010

10th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2010)

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In this paper a novel Fuzzy-based proportional integral derivative (PID) controller, which acts as an active queue manager (AQM) for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TePIIP networks. The TePIIP network is modeled as a time delayed system with a saturated input. This is because of limitations of packet dropping probability and effects of propagation delays in TePIIP networks. An improved Fuzzy algorithm is employed to derive optimal or near optimal PID control gains such that the error between the router queue length and the desired queue length is minimized. Basically, in this new methodology, the classical PID and fuzzy controller have been combined by a blending mechanism which depends on a certain function of actuating error. Simulation results show that for the same link utilization, the new hybrid fuzzy PID controller provides better performance than random early detection (RED) and PID controllers.

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Section: Simulation Modelmentioning

confidence: 99%

Fuzzy-based PID active queue manager for TCP/IP networks

Mohammadi

Pour

Jafari

et al. 2010

10th International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2010)

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“…Nevertheless, these early designs have various shortcomings including cell loss (even though cell loss is used as a congestion signal to compute the rate factor, e.g., [12]), queue size fluctuations, poor network latency, stability and low utilization. Later, FLC was used in Random Early Detection (RED) algorithm in TCP/IP networks, e.g., [13], [14], to reduce packet loss rate and improve utilization. However, they are still providing implicit or imprecise congestion signaling, and therefore cannot overcome the throughput fluctuations and conservative behavior of TCP sources.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Controller for TCP Improvement over Mobile Networks

Qasim¹,

Alisa²

2014

IJCA

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The Transmission Control Protocol (TCP) is used for reliable delivery of data over unreliable networks. Practically, most TCP mechanisms have been carefully designed for wired networks. Neglecting the characteristics of wireless environments can lead to TCP implementations with poor performance. In order to use TCP in mobile networks, improvements have been proposed in this paper to enhance TCP algorithm to distinguish between the different types of loss events. In mobile or static wireless environments, losses are not always due to network congestion, as in the case of wired networks. In this paper, a modified algorithm is presented using fuzzy controller to differentiate the loss events (error loss from congestion loss) that intend at adapting TCP to mobile and static wireless environments with better performance. Simulation results were performed using OMNET simulator and have showed that the new proposal has better throughput than other TCP schemes.

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“…Due to the practicability and robustness of fuzzy logic, the fuzzy control has attracted a great deal of interest among researchers. In fact, fuzzy logic control has proven to be a successful control approach to many complex nonlinear systems or even non-analytic systems [10][11][12][13]. Some remarkable studies on the stabilizing controller design for fuzzy systems can be referred to [14][15][16][17][18][19], in which the so-called T-S fuzzy model [20] has mainly been used to represent fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Robust fuzzy control for discrete perturbed time-delay affine Takagi-Sugeno fuzzy models

Chang

Huang

2011

Int. J. Control Autom. Syst.

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The purpose of this paper is to study the stability analysis and controller synthesis principles of Discrete Perturbed Time-Delay Affine (DPTDA) Takagi-Sugeno (T-S) fuzzy models. In general, the T-S fuzzy model is a weighted sum of some linear subsystems via fuzzy membership functions. This paper considers fuzzy rules include both linear nominal parts and uncertain parameters in the time-delay affine T-S fuzzy model. For DPTDA T-S fuzzy models, the T-S fuzzy control scheme is used to confront the H ∞ performance constraints. Some sufficient conditions are derived on robust H ∞ disturbance attenuation in which both robust stability and a prescribed performance are required to be achieved. In order to find suitable fuzzy controllers, the Iterative Linear Matrix Inequality (ILMI) algorithm is employed to solve these sufficient conditions. At last, a numerical simulation for the nonlinear truck-trailer system is given to show the applications of the present design approach.

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FAFC: fast adaptive fuzzy AQM controller for TCP/IP networks

Cited by 23 publications

References 13 publications

Fuzzy-based PID active queue manager for TCP/IP networks

Fuzzy-based PID active queue manager for TCP/IP networks

A Fuzzy Controller for TCP Improvement over Mobile Networks

Robust fuzzy control for discrete perturbed time-delay affine Takagi-Sugeno fuzzy models

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