Congestion Avoidance Using Enhanced Blue Algorithm

Vijayaraj, A.; Bhuyan, Hemanta Kumar; Raj, P. T. Vasanth; Anand, M.

doi:10.1007/s11277-022-10028-1

Cited by 10 publications

(1 citation statement)

References 31 publications

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“…Besides the previously calculated Dp and the instance queue length, the BLUE method uses three constants to normalize the Dp value: the freeze time, which represents the waiting period between two consecutive Dp modifications, the increase rate, and the decrease rate. While BLUE demonstrates good performance in terms of delay and packet loss, the limitations of the BLUE method arise in high dropping rates, which result from the harsh threshold utilized for packet dropping and the slow response in load reduction [6,7]. As such, after a bursty traffic status, which is associated with a high Dp value, high load reduction will be attended with a slow reduction in Dp value.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%