Fuzzy Proactive Queue Management Technique

Zargar, Saman Taghavi; Yaghmaee, Mohammad Hossein; Fard, Amin Milani

doi:10.1109/indcon.2006.302841

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The Fuzzy RED [36] method used Q and ∆Q as equivalent to AQL in the RED method, and the Fuzzy Explicit Marking (FEM) [37] used the same input variables with different processes. 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.…”

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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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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“…Results RED [8] Improve link utilizations NARED [33] Improve packet loss rate compared to RED LTRED [9] A slight improvement over RED, AVQ, and ARED in terms of packet loss and throughput BLUE [11] Improve packet loss rate compared to RED ERED [32] Improved throughput compared to RED and BLUE PRED [10] Improve packet loss rate compared to RED FEM [34] Ease parameterization FB [35] Ease parameterization FGREEN [36] Ease parameterization FConRED [37] Ease parameterization GRED [5] Improve packet loss rate compared to RED ARED [44] Improve packet loss rate compared to RED FRED [48] Ease parameterization AVQ [29] Improve packet loss rate compared to RED SAVQ [30] Improve packet loss rate compared to RED EAVQ [31] Improve packet loss rate compared to RED SRED [23] Improve packet loss rate compared to RED FuzzyRED [37] Ease parameterization FLRED [4] Ease parameterization 4 Results…”

Section: Methodsmentioning

confidence: 99%

“…Among these components, the input variables are the most critical components for the fuzzy-based AQM. Examples of these methods are Fuzzy Explicit Marking (FEM) [34], Fuzzy BLUE (FB) [35], Fuzzy GREEN (FGREEN) [36], Fuzzy-logic Controller-based RED (FConRED) [37], and FLRED [4]. Although these methods were created to ease parameter initialization, other objectives can be achieved based on the input variables to be utilized, such as AQL [34], delay [4], and packet loss [35].…”

Section: Previous Workmentioning

confidence: 99%

Linear Random Early Detection for Congestion Control at the Router Buffer

Abu-Shareha¹,

Al-Kasasbeh²,

Shambour³

et al. 2022

IJCAI

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Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. Povzetek: Opisan je razvoj nove metode za upravljanje predpomnilnika na omrežju.

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“…The advantage of the decision fuzziness, the fuzzy-based AQM methods enable an easy extension into many cases and straightforwardly use multiple indicators. Various fuzzy-based methods have been proposed [42,45]. These methods have solved the parameterization problem, added more flexibility to the developed methods, and enhanced the results under certain traffic conditions.…”

Section: Introductionmentioning

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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Fuzzy Proactive Queue Management Technique

Cited by 6 publications

References 14 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

Linear Random Early Detection for Congestion Control at the Router Buffer

Fuzzy Comprehensive Random Early Detection of Router Congestion

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