MRED: a new approach to random early detection

Koo, Jahon; Song, Byunghun; Chung, Kwangsue; Lee, Hyukjoon; Kahng, Hyun-Kook

doi:10.1109/icoin.2001.905450

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Overall, ARED is similar GRED in responsiveness and flexibility and is more adaptive than GRED and RED. RED GRED, and ARED used the same status variables and variations of the dropping schem Effective RED (ERED) [16] used AQL and Q. Multi-level RED (MRED) [17] used pack loss (PL), while the Adaptive Virtual Queue (AVQ) [18], Stabilized AVQ (SAVQ) [19], an Enhanced AVQ (EAVQ) [20] used the arrival rate. Link Utilization Based AQM (LUBA [21], Stabilized Virtual Buffer (SVB) [22], Rate-Based AQM (RAQM) [23], Robust Activ Queue (RaQ) [24], and Yellow [25] used load rate and arrival rate.…”

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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“…RED was made to work with TCP connections so that dropped packets are retransmitted with no loss of information but it also causes the TCP sending window to shrink. Although these issues were later addressed by the research community, RED also suffers from performance drops as RED behavior still allows queue overflows [13].…”

Section: Methodsmentioning

confidence: 99%

Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks

et al. 2023

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“…Although RED has resolved the fairness issue in DT, its basic operation that randomly marks and discards packets to avoid congestion, does not protect the packets that deserve transmission. Further improvements of RED is MRED [30,31] which considers extra parameters for dropping probability. However, these extra parameters are still insufficient to handle network congestion.…”

Section: Related Workmentioning

confidence: 99%

Multi‐objective optimisation for selective packet discarding in wireless sensor network

Yaakob

Khalil

Atiquzzaman

2015

IET wirel. sens. syst.

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Traffic convergence in wireless sensor networks (WSN) during simultaneous data transmission may overwhelm its limited buffer capacity, resulting in congestion, waste of resources and severe performance degradation. The obvious consequences include high packet loss rate, huge amounts of wasted energy and obsolete data that may lead to inaccurate information. Since WSN suffers from scarce resources such as energy, data transmission which is the main cause of energy depletion should be kept to the very minimum. Various studies have used packet discarding as a means to reduce high traffic volumes. However, none of these methods have ever been applied in WSN which possesses different characteristics. This study proposes a new technique for mitigating congestion by selectively discarding some of the least important packets to give sufficient room for more important ones to get through. The proposed discarding policy is integrated with multiobjective optimisation (MOO) which can optimise several objectives at once. The proposed selective packet discarding policy discards the unimportant packets based on some discarding criteria which will be optimised by the MOO. Performance evaluation using the optimisation tool (LINGGO) and simulation in Network Simulator 2 shows remarkable and promising performance with more than 50% improvement.

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MRED: a new approach to random early detection

Cited by 12 publications

References 3 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

Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks

Multi‐objective optimisation for selective packet discarding in wireless sensor network

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