Flexible Random Early Detection Algorithm for Queue Management in Routers

2020 ◽  
pp. 196-208
Author(s):  
Aminu Adamu ◽  
Vsevolod Shorgin ◽  
Sergey Melnikov ◽  
Yuliya Gaidamaka
Keyword(s):  
Early Detection ◽  
Detection Algorithm ◽  
Queue Management ◽  
Random Early Detection

scholarly journals SYMBOLIC DYNAMICAL MODEL OF AVERAGE QUEUE SIZE OF RANDOM EARLY DETECTION ALGORITHM

2010 ◽  
Vol 20 (05) ◽  
pp. 1415-1437 ◽  
Author(s):  
CHARLOTTE YUK-FAN HO ◽  
BINGO WING-KUEN LING ◽  
HERBERT H. C. IU
Keyword(s):  
Fixed Point ◽  
Early Detection ◽  
Initial Conditions ◽  
Detection Algorithm ◽  
Dynamical Model ◽  
Random Early Detection ◽  
Queue Size ◽  
System Parameters ◽  
Internet Networks

In this paper, a symbolic dynamical model of the average queue size of the random early detection (RED) algorithm is proposed. The conditions for both the system parameters and the initial conditions that the average queue size of the RED algorithm would converge to a fixed point are derived. These results are useful for network engineers to design both the system parameters and the initial conditions so that internet networks can achieve a good performance.

scholarly journals Active Queue Management (AQM) Performance Analysis Based On Controled Delay (CoDel) Against Bufferbloat On Real-Time Aplication

2017 ◽  
Vol 2 (1) ◽  
pp. 119
Author(s):  
Muhammad Noer Iskandar
Keyword(s):  
Performance Analysis ◽  
Early Detection ◽  
Real Time ◽  
Video Streaming ◽  
Packet Loss ◽  
Active Queue Management ◽  
Queue Management ◽  
Random Early Detection

<span><em>Bufferbloat </em><span>merupakan salah satu kondisi buffer dengan ukuran besar yang cenderung<br /><span>selalu penuh dan menyebabkan antrian panjang didalam buffer, jika hal ini terjadi secara<br /><span>terus-menerus maka dapat menyebabkan jeda transmisi yang tinggi. <span><em>Bufferbloat </em><span>sering<br /><span>terjadi pada aplikasi berbasis real-time. <span><em>Active Queue Management </em><span>(AQM) merupakan<br /><span>salah satu cara untuk menangani terjadinya <span><em>bufferbloat., </em><span>AQM umumnya menggunakan<br /><span>algoritma Drop Tail untuk menangani kondisi antrian panjang dalam buffer router di<br /><span>jaringan. Namun demikian, performansi AQM berbasis Drop Tail kurang dapat<br /><span>diandalkan karena jeda transmisi dalam keadaan <span><em>bufferbloat </em><span>masih tinggi. Telah banyak<br /><span>studi dilakukan untuk menangani <span><em>bufferbloat</em><span>, seperti Drop Tail, Random Early Detection<br /><span>(RED) dan Controlled Delay (CoDel). Dari riset yang telah dilakukan tersebut masih sulit<br /><span>ditemukan performasi algoritma terbaik dalam menangani <span><em>bufferbloat</em><span>. Untuk hal tersebut,<br /><span>paper ini menyajikan studi performansi penanganan bufferbloat menggunakan ketiga<br /><span>algoritma diatas. Dalam studi ini, video streaming digunakan sebagai <span><em>traffic </em><span>uji untuk<br /><span>menentukan performansi algoritma terbaik dalam mengatasi <span><em>bufferbloat</em><span>. Sedangkan<br /><span>metriks uji yang digunakan dalam riset ini adalah <span><em>latency</em><span>, <span><em>throughput </em><span>dan <span><em>packet-loss</em><span>.<br /><span>Analisa hasil pengujian mengambil 3 hasil terbaik dalam setiap percobaan. Hasil<br /><span>pengujian menunjukan performansi algoritma CoDel jauh lebih baik dalam menangani<br /><span><em>latency </em><span>yang tinggi pada kondisi bufferbloat dibandingkan RED dan Drop Tail. Namun<br /><span>untuk <span><em>packet-loss </em><span>dan <span><em>throughput </em><span>performansi RED dan Drop Tail masih unggul<br /><span>dibanding algoritma CoDel</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>

FRED - fair random early detection algorithm for TCP over ATM networks

1998 ◽  
Vol 34 (2) ◽  
pp. 152 ◽  
Author(s):  
Woo-June Kim ◽  
Byeong Gi Lee
Keyword(s):  
Early Detection ◽  
Detection Algorithm ◽  
Atm Networks ◽  
Random Early Detection

scholarly journals EFRED: Enhancement of Fair Random Early Detection Algorithm

2015 ◽  
Vol 08 (07) ◽  
pp. 282-294 ◽  
Author(s):  
Muntadher Abdulkareem ◽  
Kassem Akil ◽  
Ali Kalakech ◽  
Seifedine Kadry
Keyword(s):  
Early Detection ◽  
Detection Algorithm ◽  
Random Early Detection
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