Fuzzy Comprehensive Random Early Detection of Router Congestion

Abu-Shareha, Ahmad Adel; Al-Kasasbeh, Basil; Shambour, Qusai; Abualhaj, Mosleh M.; Al-Khatib, Sumaya N.

doi:10.5755/j01.itc.51.2.30194

Cited by 2 publications

(4 citation statements)

References 33 publications

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“…The Proportional Integral (PI) controller [13], which is among the most utilized AQM methods, extends RED by incorporating delay as a congestion indicator. The experiments proved that RED and PI outperformed the drop-tail but still suffer from unnecessary packet dropping and packet loss [8].…”

Section: The Related Workmentioning

confidence: 94%

“…However, these methods exhibit performance issues under bursty traffic conditions. The adaptive methods stabilize the AQM performance by enabling self-adaptation based on traffic or queue status [8]. The problem with these methods is using equal increasing and decreasing rates, leading to a slow response in sudden network load changes.…”

Section: The Related Workmentioning

confidence: 99%

“…A low decreasing rate characterizes the slow reduction of Dp in BLUE. While a high reduction rate might seem like a potential solution, it could introduce instability in the algorithm dealing with the bursting nature of the traffic [8].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Related Workmentioning

confidence: 94%

Section: The Related Workmentioning

confidence: 99%

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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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“…Fuzzy BLUE (FBLUE) [38] used buffer size and packet loss as input variables, Fuzzy GREEN [39] used Q and dropping rate, Fuzzy-logic Controller-based RED [40] used AQL and Pl, and Fuzzy-logic RED (FLRED) [41] used the delay and AQL as the input variables. Fuzzy Comprehensive RED (FCRED) [42] used three indicators, which monitor the router's arrival, departure, and queue length. Table 2 summarizes the existing work on fuzzy-based AQM methods.…”

Section: The Fuzzy-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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Fuzzy Comprehensive Random Early Detection of Router Congestion

Cited by 2 publications

References 33 publications

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

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

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

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