Open Access

ARTICLE

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

by Ali Alshahrani¹, Ahmad Adel Abu-Shareha^2,*, Qusai Y. Shambour², Basil Al-Kasasbeh¹

1 Faculty of Computer Studies, Arab Open University, Riyadh, 11681, Saudi Arabia
2 Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19111, Jordan

* Corresponding Author: Ahmad Adel Abu-Shareha. Email:

Computers, Materials & Continua 2023, 77(2), 1679-1698. https://doi.org/10.32604/cmc.2023.043545

Received 05 July 2023; Accepted 18 September 2023; Issue published 29 November 2023

Abstract

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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Keywords

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