Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

Abualhaj, Mosleh M.; Al-Tahrawi, Mayy M.; Hussein, Abdelrahman H.; Al-Khatib, Sumaya N.

doi:10.2478/cait-2021-0017

Cited by 4 publications

(5 citation statements)

References 22 publications

(22 reference statements)

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“…One example of such solutions is the use of nonlinear differential equations to describe the dynamics of changes in the utilization of interface queues, obtained on the basis of various approximations [4], [5]. According to Abualhaj et al [6], the possibility of using neural networks and fuzzy logic methods for organizing queue management is substantiated. A compromise option from the point of view of the level of adequacy of the mathematical description of queue management processes and the expected computational complexity of the resulting solutions can be the approach presented in [7], [8].…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

“…Queue balancing is based on the introduction of appropriate metrics, and the applied network problem is reduced to an optimization problem of linear programming [10], [11]. These models in [5], [6] do not take into account the utilization of the router interface queues by their length. A model of dynamic guaranteed queuing is considered [14]- [19].…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

“…Characterize the conditional cost (𝑠 𝑖𝑗 ) of using the resources of the j-th queue by packets of the i-th traffic, as well as the cost (𝑠 𝑗 ) of allocating the j-th queue of one or another amount of bandwidth of the outgoing data transmission channel. According to Abualhaj et al [6], for the purpose of balanced utilization of buffer resources, the following condition is additionally introduced:…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

“…Where 𝑡 𝑖𝑝 and 𝑃 𝑖𝑝 are the allowable values of the average delay and packet loss of the i-th flow in the network node. If each queue is an exponential queuing system with a limited queue of the M/M/1/L type [5], [6], then the probability of packet loss due to buffer overflow is defined by the following expression:…”

Section: The Proposed Model For the Distribution Of Channel And Buffe...mentioning

confidence: 99%

See 3 more Smart Citations

Model of optimal distribution of network resources with constraints on quality of service indicators

Ulugbek

Azamat

2023

Bulletin EEI

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Existing algorithms and mathematical models of queuing at the nodes of a telecommunications network are considered in this paper. The necessity of coordinated solutions to problems of distribution of channel and buffer resources of the network is shown. A model for the optimal distribution of channel and buffer resources on network nodes has been developed. The optimization (minimization) criterion is the total average packet delay with constraints on the quality of service (QoS) indicators of heterogeneous flows. The optimization problem is presented as a constrained nonlinear programming problem and solved using the “fmincon” program of the optimization toolbox MATLAB package.

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Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

Section: The Proposed Model For the Distribution Of Channel And Buffe...mentioning

confidence: 99%

See 2 more Smart Citations

Model of optimal distribution of network resources with constraints on quality of service indicators

Ulugbek

Azamat

2023

Bulletin EEI

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“…The literature [24] presents an improved AQM algorithm based on long short-term memory (LSTM) networks, leveraging LSTM's characteristics to predict and compensate for network delays, exhibiting good responsiveness in dynamic networks. In recent years, active queue management algorithms based on fuzzy logic control [25,26] and optimization theory [27,28] have been proposed.…”

Section: Related Workmentioning

confidence: 99%

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

Zhu,

Sun,

Jiang

et al. 2023

Axioms

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The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

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A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

et al. 2022

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The rapid development of information technology has promoted the transformation of traditional networks into intelligent networks. Huge data traffic is generated by various types of traffic services in the intelligent networks, which can easily lead to network congestion, system instability, and other problems. These problems may incur great requirements and pose challenges for queue management algorithms. Most traditional active queue management (AQM) algorithms judge the congestion level of the network based only on the size of the average queue length while ignoring the network traffic variations. This makes these algorithms difficult to achieve effective improvement of congestion control efficiency. To address this problem, a novel network congestion control algorithm, namely the average queue length and change rate -RED (AC-RED) is proposed in this paper. AC-RED can better relieve network congestion by reconfiguring the packet loss strategy model based on the average queue length change rate. The simulation results of NS2 show that in complex and dynamic network environments, the performance of AC-RED algorithm in the average queue length, packet loss rate, delay, and delay jitter is improved in most load conditions compared with the other five algorithms.INDEX TERMS Active queue management; network congestion; average queue length change rate; AC-RED; NS2

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Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

Cited by 4 publications

References 22 publications

Model of optimal distribution of network resources with constraints on quality of service indicators

Model of optimal distribution of network resources with constraints on quality of service indicators

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

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