Robust Active Queue Management for Wireless Networks

Chavan, Kanchan; Kumar, Rajiv; Belur, Madhu N.; Karandikar, Abhay

doi:10.1109/tcst.2010.2081671

Cited by 40 publications

(26 citation statements)

References 22 publications

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“…Chavan [10] has given the algorithm to calculate the p into seven steps. First, initialize the operating points Wo , qo .…”

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

“…Po ,Co then design the controller H fo llowed by measuring the instantaneous queue length of the router at every packet arrival. After determining the deviation queue length �q, change in dropping probability �p may be calculated according to the fo rmula given in equation (2) [10].…”

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

“…Chavan et al [10] have successfully addressed the problem related to the variation of link capacity in the wireless networks.…”

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

“…UDP applications are depends on the Internet. There have been several methods which are proposed to manage the Internet traffic to control the Internet resources [1-7] & [9][10]. AQM is one of the major approaches.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis and modeling of congestion control algorithms based on active queue management

Patel

2013

2013 International Conference on Signal Processing and Communication (Icsc)

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We have considered various types of congestion control algorithms. Each congestion algorithm has its own advantages and it would vary fr om parameters to parameters. Random Early Detection (RED) is more fo cused on queue length and BLUE is care about loss of packets. In this paper we have fo und the problems with existing congestion control algorithms. We have tried to show their performance of RED, SFQ, and REM in terms of performance parameters i.e. delay, throughput, loss rate etc. for our considered network configurations. Similarly BLUE and Random Exponential Marking (REM) are more fo cused on packet loss and mismatch respectively, mismatch occurring in REM due to either input rate and link capacity or queue length and target. In order to restrict the rising packet loss rates caused by network traffic, active queue management technique such as REM has come into picture. Flow Random Early Drop (FRED) keeps state information based on instantaneous queue occupancy of a given flow. Stochastic Fair Queuing (SFQ) ensures fair access to network resources and prevents a busty flow fr om consuming more than its fair share. Stabilized RED (SRED) is another approach of detecting nonresponsive flows. In this paper, we proposed a model to calculate dropping probability and packet loss for Active Queue Management (AQM). At the last, we have shown a comparative analysis of the loss delay product (LDP) as a new parameter of performance measure obtained fr om simulation on ns2 for different AQM algorithms. It has been observed that performance parameters are varying according to the various congestion algorithms used in the simulation. RED achieved the best result in terms of the delay but in terms of throughput, loss ratio, and utilization REM shows the best results in this network configuration. But, RED performed best at low link capacity in terms of new measured parameter LDP.Keyword-Random early detection (R ED) , random exp onential marking (R EM), BL UE, active queue management (A QM), dropp ing probability, throughput, loss delay product (L DP), stochastic fa ir queuing (S FQ), first in first out (FIFO) , delay, queue length, loss rate, and utilization.

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“…Chavan [10] has given the algorithm to calculate the p into seven steps. First, initialize the operating points Wo , qo .…”

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

“…Chavan et al [10] have successfully addressed the problem related to the variation of link capacity in the wireless networks.…”

Section: Robust Active Queue Managament (R Aqm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance analysis and modeling of congestion control algorithms based on active queue management

Patel

2013

2013 International Conference on Signal Processing and Communication (Icsc)

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“…The robustness property of the H controllers makes them potentially applicable for achieving robust performance despite the network uncertainties in link bandwidth C, RTT, and link load N. The controller introduced in [18] has been designed by assuming that the link bandwidth is the only uncertainty in the network. In addition, the controller has been designed for a wireless network in [19] by assuming varying link bandwidth as the disturbance only. The based controllers have been studied for the AQM using the -optimal approach (e.g., [20]) or the loop-shaping technique (e.g., [21]) to accommodate control plant uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Internet traffic control with the H<inf>2</inf>/H<inf>∞</inf> controller

Haghighizadeh

Yang

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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This paper studies the H 2 /H approach to control the Internet traffic. We are interested in using the H 2 formulation to improve transient performances in addition to maintaining the robust performance offered by the formerly used H formulation. In this controller, the closed-loop dynamics are monitored and controlled by the sensitivity functions along with their associated weight functions. The LMI approach is used to solve the optimization problem under the H 2 /H formulation. Our performance evaluation via simulation demonstrated that our robust H 2 /H controller is robust and can provide significant improvements in transient response and steady state performance when compared to the H controller.

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Congestion tracking control for wireless TCP/AQM network via immersion and invariance

Poonyanirun

Kanchanaharuthai

2022

Asian Journal of Control

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This paper deals with an immersion and invariance (I&I) control design for wireless Transmission Control Protocol (TCP) network to solve Active Queue Management (AQM) problems. With the help of this strategy, the developed control law can be used to cope with congestion tracking problems for a nonlinear wireless network model. Besides, the nonlinear congestion control law is proposed to assure that the queue length is able to track the desired queue length despite having sudden changes. The developed control design is validated in the MATLAB environment. The effectiveness of the developed control is verified through a simulation and compared with integral backstepping design, backstepping design, sliding mode design, and synergetic control design. The simulation results demonstrated that the presented strategy is not only able to solve the desired congestion tracking control problem but also provide improved transient performances.

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Robust Active Queue Management for Wireless Networks

Cited by 40 publications

References 22 publications

Performance analysis and modeling of congestion control algorithms based on active queue management

Performance analysis and modeling of congestion control algorithms based on active queue management

Internet traffic control with the H<inf>2</inf>/H<inf>∞</inf> controller

Congestion tracking control for wireless TCP/AQM network via immersion and invariance

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Robust Active Queue Management for Wireless Networks

Cited by 40 publications

References 22 publications

Performance analysis and modeling of congestion control algorithms based on active queue management

Performance analysis and modeling of congestion control algorithms based on active queue management

Internet traffic control with the H<inf>2</inf>/H<inf>&#x221E;</inf> controller

Congestion tracking control for wireless TCP/AQM network via immersion and invariance

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Internet traffic control with the H<inf>2</inf>/H<inf>∞</inf> controller