Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

Hassan, Samuel; Rufai, Adewole Usman; Nwaocha, Vivian; Ogunlere, Samson Ojo; Adegbenjo, Aderonke Adelola; Agbaje, M. O.; Enem, Theophilus Aniemeka

doi:10.11591/ijece.v13i1.pp669-679

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Finally, if the calculated AQL is higher than the second threshold, the dropping probability is set to one in such a case, as the network flow is considered aggressive. Figure 3 illustrates the status variable and the dropping mechanism implemented in RED [13].…”

Section: The Crisp-based Aqm Methodsmentioning

confidence: 99%

“…Finally, if the calculated AQL is higher than the second threshol the dropping probability is set to one in such a case, as the network flow is considere aggressive. Figure 3 illustrates the status variable and the dropping mechanism impl mented in RED [13]. Various crisp-based methods were proposed using different variables and mech nisms compared to the one implemented by the RED.…”

Section: The Crisp-based Aqm Methodsmentioning

confidence: 99%

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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%

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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show abstract

“…Adaptive Queue Management with Random Dropping (AQMRD) [27] uses the average and instance queue lengths for buffer management. The stability of RED (S-RED) [28] reduces the loss by using a quadratic function to make its variability smoother compared to RED [29].…”

Section: The Related Workmentioning

confidence: 99%

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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“…In the literature, many types of function d(i) are used in AQM, e.g., linear [12], quadratic [13], sinusoidal [14], cubic [15,16], exponential [17], beta [18], and Gaussian functions [9]. Additionally, some researchers propose d(i) in a combined form, consisting of two linear functions [19], linear and quadratic functions [20], linear and cubic functions [21], linear and exponential functions [22], or quadratic and exponential functions [23]. Herein, we adopt function d(i) in a general form.…”

Section: Introductionmentioning

confidence: 99%

“…As far as the author knows, the results of this paper are new. Active queue management, which rejects packets basing on the buffer occupancy, was recommended in [9,[12][13][14][15][16][17][18][19][20][21][22][23]. However, all of these studies are based on simulations rather than on queuing theory.…”

mentioning

confidence: 99%

Level-Crossing Characteristics of an Actively Managed Buffer

Chydzinski

2024

JSAN

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In this paper, we examine a buffer with active management that rejects packets basing on the buffer occupancy. Specifically, we derive several metrics characterizing how effectively the algorithm can prevent the queue of packets from becoming too long and how well it assists in flushing the buffer quickly when necessary. First, we compute the probability that the size of the queue is kept below a predefined level L. Second, we calculate the distribution of the amount of time needed to cross level L, the buffer overflow probability, and the average time to buffer overflow. Third, we derive the distribution of the amount of time required to flush the buffer and its average value. A general modeling framework is used in derivations, with a general service time distribution, general rejection function, and a powerful model of the arrival process. The obtained formulas enable, among other things, the solving of many design problems, e.g., those connected with the design of wireless sensor nodes using the N-policy. Several numerical results are provided, including examples of design problems and other calculations.

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Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

Cited by 6 publications

References 28 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

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

Level-Crossing Characteristics of an Actively Managed Buffer

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