scholarly journals Pricing and Unresponsive Flows Purging for Global Rate Enhancement

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
G. Abbas ◽  
A. K. Nagar ◽  
H. Tawfik ◽  
J. Y. Goulermas
Keyword(s):  
Early Detection ◽  
Active Queue Management ◽  
Low Complexity ◽  
Rate Allocation ◽  
Queue Management ◽  
Random Early Detection ◽  
Rate Enhancement ◽  
Global Rate ◽  
High Utilization ◽  
Unresponsive Flows

Pricing-based Active Queue Management (AQM), such as Random Exponential Marking (REM), outperforms other probabilistic counterpart techniques, like Random Early Detection (RED), in terms of both high utilization and negligible loss and delay. However, the pricing-based protocols do not take account of unresponsive flows that can significantly alter the subsequent rate allocation. This letter presents Purge (Pricing and Un-Responsive flows purging for Global rate Enhancement) that extends the REM framework to regulate unresponsive flows. We show that Purge is effective at providing fairness and requires small memory and low-complexity operations.

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>

scholarly journals An Exponential Active Queue Management Method Based on Random Early Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hussein Abdel-Jaber
Keyword(s):  
Early Detection ◽  
Performance Metrics ◽  
Active Queue Management ◽  
Queue Management ◽  
Random Early Detection ◽  
Evaluation Performance ◽  
Queuing Delay ◽  
Packet Dropping ◽  
Dropping Probability ◽  
Évaluation Performance

Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

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