Integrated Random Early Detection for Congestion Control at the Router Buffer

Abu-Shareha, Ahmad Adel

doi:10.32604/csse.2022.018369

Cited by 6 publications

(3 citation statements)

References 30 publications

(34 reference statements)

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“…Linear RED (LRED) [34] developed a linear equation with calculations to reduce the complexity of RED. Integrated RED (IRED) [35] used arrival, departure, and queue length factors to improve the AQM performance. Table 1 summarizes the existing work on crisp-based AQM methods.…”

Section: The Crisp-based Aqm Methodsmentioning

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 Crisp-based Aqm Methodsmentioning

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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“…The differences are embodied in Information Technology and Control 2022/2/51 256 how these indicators are calculated and updated. Estimated loss and estimated delay, commonly used by current methods, differ in the definition and calculation [5].…”

Section: Previous Workmentioning

confidence: 99%

Fuzzy Comprehensive Random Early Detection of Router Congestion

Abu-Shareha

Al-Kasasbeh²,

Shambour³

et al. 2022

ITC

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The queue length and the load rate should be monitored to overcome the problem of router congestion due to the increase in network utilization and achieve a high-speed transmission. Previous active queue management methods manage the queued packets in the router buffer to maintain high network performance. However, these methods depend on monitoring indicators that do not cover all the congestion signs, leading to packet loss and delay. Accordingly, all the congestion signs should be wrapped into these indicators and managed by an algorithm that randomly drops packets to avoid global synchronization, loss, and delay. In this paper, a fuzzy comprehensive random early detection (FCRED) is proposed to deal with the gap in network monitoring and congestion control at the router buffer. FCRED is built by using three indicators, which monitor the router's arrival, departure, and queue length. Accordingly, a fuzzy inference process is developed to manage these indicators and calculate the dropping probability (Dp). Simulation results show that FCRED improves loss and packet dropping under various network statuses compared with RED, BLUE, and ERED. In terms of loss, FCRED achieves zero loss at high congested status. For dropping, FCRED achieves an optimal rate of 0.47 with an arrival rate of 0.95. For the throughput and delay, FCRED achieves the best results. Accordingly, the proposed FCRED method achieves zero loss and reduces packet dropping from 0.28 to 0.21, a 25% reduction compared with the best performance of these methods. Compared with recent fuzzy-based methods, the proposed FCRED achieves comparable results and outperforms them by dropping more packets to avoid loss, which in such case is necessary dropping.

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“…Accordingly, the BLUE method used a mechanism of two cases formulated using IF-THEN. 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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Integrated Random Early Detection for Congestion Control at the Router Buffer

Cited by 6 publications

References 30 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

Fuzzy Comprehensive Random Early Detection of Router Congestion

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

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