Stability analysis of active queue management techniques

Mohapatra, Sunil Kumar; Bisoy, Sukant Kishoro; Dash, Prasant Kumar

doi:10.1109/mami.2015.7456598

Cited by 6 publications

(12 citation statements)

References 15 publications

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“…[25] This algorithm was proposed by Floyd and Jacobson to solve the problems which occurred in passive queue management techniques. [26] The mechanism monitors the size of the queue and randomly marks or drops the data packet depending on the statistical probability of congestion in the network.…”

Section: Red (Random Early Detection)mentioning

confidence: 99%

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

Sahu¹,

Mahapatro²,

Dash³

2018

IJET

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The world is running in a lightning speed with the rapid up gradation of technology. In fraction of second human civilization experiences a unique set of technologies and for this human sentiment started to demand better quality of service in almost all the areas which provides ease to it by minimizing human work efforts. Communication becomes an integral part of this rapidly changing technology. From the introduction of internet, the taste and preferences of virtual communication has been changed. A new technology called as Mobile Ad Hoc Network (MANet) is also getting its popularity due to its infrastructure less and mobility property. The rapid increase in demand for use of MANets tends it to further development to provide better quality of service. However Congestion in these networks creates a challenge to achieve desired Quality of Service. Different Queue Management techniques such as Passive and Active Queue management techniques are used to minimize the congestion in these networks which uses the concept of managing the buffers though algorithms. The Passive Queue Management technique Droptail, in the introduction stage, somehow manages to increase its efficiency. However in later stage, when data traffic increases, due to global synchronization problem many other algorithms including active queue management techniques such as RED and variants, REM have been developed to increase the efficiency by decreasing the congestion. The concepts even travel from congestion control to congestion avoidance. In this paper we have taken into account different passive and active queue management techniques and compared them based on our literature review to find out the best among them by considering different situations.

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Section: Red (Random Early Detection)mentioning

confidence: 99%

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

Sahu¹,

Mahapatro²,

Dash³

2018

IJET

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“…. , 5), X i .0(i = 1, 2), Y .0, n 3 n diagonal matrices L.0, X.0, F.0, P.0 and given the external interference attenuation level g, that the following conditions in (13) and (14) hold 2 6 6 6 6 6 6 6 6 6 6 6 6 4 3 7 7 7 7 7 7 7 7 7 7 7 7 5 \0 ð14Þ where…”

Section: Model Formulations Of Desired Cluster Synchronization With Tmentioning

confidence: 99%

“…where O is respectively expressed in equation 18, and z Based on O\0. in equation (14) and convex combination technique that _ V (t) 0 and _ V (t) = 0 if and only if w(t) = 0.…”

Section: Model Formulations Of Desired Cluster Synchronization With Tmentioning

confidence: 99%

“…with z T (t) = ½e T (t), e T (t À t 0 ), e T (t À t(t)), e T (t À t m ), _ e T (t), f T (C T e(t)), f T (C T e(t À t(t))), w T (t) Following the inequalities in equations (13) and (14), and from Schur Complement Lemma, we have…”

Section: Model Formulations Of Desired Cluster Synchronization With Tmentioning

confidence: 99%

“…Wireless multimedia sensor networks (WMSNs) consist of interconnected sensor nodes that are capable of continuously retrieving multimedia content such as video and audio streams, images, and sensor data. [1][2][3] Excessive amount of data transmission in CWMSNs may cause network congestion, 4,5 and many recent researches [1][2][3][6][7][8][9][10] concern this issue and come up with congestion control schemes to solve this problem, 11,12 where traditional source-side congestion control methods such as active queue management (AQM), 13,14 random early detection (RED), 15,16 and additive-increase multiplicative-decrease (AIMD) 17,18 are applied. However, the traditional ones do not guarantee network congestion control stability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A robust congestion control scheme for cluster wireless multimedia sensor networks with propagation delay and external interference

Wei

2016

International Journal of Distributed Sensor Networks

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Congestion control in cluster wireless multimedia sensor networks is capable of delivering and gathering multimedia information, while propagation delay and external interference may cause it unable to achieve the maximal network throughput without congestion control. To solve the problem, based on the additive-increase multiplicative-decrease adjustment strategy, a robust congestion control model is proposed with a scheduling scheme in cluster wireless multimedia sensor networks considering the neighbor feedback. The robust congestion control model is a Lur'e-type system. The cluster wireless multimedia sensor network is capable of achieving the desired cluster synchronization with network parameters provided by Lyapunov-Krasovskii functionals. Sufficient conditions of the robust congestion control are formulated by linear matrix inequalities. Numerical results show the effectiveness of the proposed scheme.

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Quality of service aware adaptive target queue length generation for active queue management

Hotchi

Kubo

2021

IET Control Theory & Appl

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Active queue management techniques have been proposed to mitigate serious congestion issues caused by transmission control protocol flows. Active queue management controllers based on control theory are superior in terms of control performance. Such a controller utilizes a target queue length as a control input, and the congestion control system attempts to maintain the actual queue length close to the target value. However, the specific setting of the target queue length has not been discussed sufficiently. For example, raising or lowering the target queue length may increase goodput or reduce queueing delay, respectively. Therefore, the target queue length should be appropriately generated according to the required quality of services and buffer status. This paper proposes a quality of services–aware adaptive target queue length generation algorithm for an active queue management controller based on control theory. The proposed algorithm comprises two modes (loss‐aware and delay‐aware modes) and attempts to raise or lower the target queue length by considering the buffer status to increase the goodput or reduce the queueing delay. Simulation results revealed that the proposed algorithm could increase goodput or reduce queueing delay on each mode by adapting to the buffer status, which is in contrast to conventional methods.

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Stability analysis of active queue management techniques

Cited by 6 publications

References 15 publications

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

A robust congestion control scheme for cluster wireless multimedia sensor networks with propagation delay and external interference

Quality of service aware adaptive target queue length generation for active queue management

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