Active queue management for alleviating Internet congestion via a nonlinear differential equation with a variable delay

Mounier, Hugues; Join, Cédric; Delaleau, Emmanuel; Fliess, Michel

doi:10.1016/j.arcontrol.2023.02.002

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…Within the AQM, these decision rules function as mappings, translating observed values of status variables into suitable actions related to dropping packets. The typical decision-making steps within the AQM process are illustrated in Figure 1 [12]. Existing AQM methods are broadly categorized into two main types: crisp-based AQM and fuzzy-based AQM.…”

Section: The Literature Reviewmentioning

confidence: 99%

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Abu-Shareha,

Alsaaidah,

Alshahrani

et al. 2023

Symmetry

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Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

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Section: The Literature Reviewmentioning

confidence: 99%

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Abu-Shareha,

Alsaaidah,

Alshahrani

et al. 2023

Symmetry

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“…It is worth mentioning that only linear approximations with constant delays have been mostly used to propose various AQMs such as RED, PIE, and those proposed in [ 34 , 35 ]. H. Mounier et al [ 36 ] propose an AQM control-theoretic method in which the constraint of assuming a constant delay, i.e. the round trip time has been lifted and a non-linear model with a variable delay has been introduced.…”

Section: Bbr V2 and Bbr-nmentioning

confidence: 99%

TCP BBR-n: Increased throughput for wireless-AC networks

Ahsan,

Muhammad

2023

PLoS ONE

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Google proposed a new TCP congestion control algorithm (CCA), Bottleneck Bandwidth and Round-trip propagation time (BBR) which has opened up new dimensions in congestion control. BBR tries to operate near Kleinrock’s operating point to avoid excessive queue formation at the bottleneck and to use the link bandwidth optimally. BBR creates a model of the network path by measuring the bottleneck bandwidth and minimum round-trip time (RTT) to maximize the delivery rate and minimize latency. BBR v2 is an updated version of BBR which addresses many shortcomings of the original BBR (BBR v1) such as interprotocol fairness, RTT fairness, and excessive retransmissions. However, BBR v2 has certain limitations in its operation in IEEE 802.11ac (Wi-Fi 5) networks. The default BBR v2 limits the throughput of Wi-Fi 5 and an increased latency has been observed. This is because the Wi-Fi 5 frame aggregation logic is underutilized and fewer frames are being sent to the Wi-Fi 5 interface. In this paper, we have proposed BBR-n (BBR new) which provides better throughput than the generic BBR v2 in the Wi-Fi 5 networks. Real-time experiments were performed over a physical testbed using Flent to confirm that BBR-n achieves over double throughput as compared to generic BBR v2 and reduced latency in networks as compared to pure loss-based variants such as Cubic and Reno.

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“…The first case is for increasing Dp, and the other is for decreasing it [4]. There is a case left open in which none of the previous cases occurs; in such a case, Dp remains unchanged [5].…”

Section: Introductionmentioning

confidence: 97%

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

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Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.

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Active queue management for alleviating Internet congestion via a nonlinear differential equation with a variable delay

Cited by 10 publications

References 38 publications

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

TCP BBR-n: Increased throughput for wireless-AC networks

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

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