A random early detection based active queue management algorithm in power optical communication network

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

Indonesian Journal on Computing (Indo-JC) ◽

10.21108/indojc.2017.2.1.139 ◽

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

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

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Pricing and Unresponsive Flows Purging for Global Rate Enhancement

Journal of Electrical and Computer Engineering ◽

10.1155/2010/379652 ◽

2010 ◽

Vol 2010 ◽

pp. 1-10 ◽

Cited By ~ 11

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.

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Receiver-Window Modified Random Early Detection (RED-RWM) Active Queue Management Scheme: Modeling and Analysis

10.1109/icc.2006.254721 ◽

2006 ◽

Cited By ~ 1

Author(s):

Visvasuresh Govindaswamy ◽

Gergely Zaruba ◽

G. Balasekaran

Keyword(s):

Early Detection ◽

Active Queue Management ◽

Queue Management ◽

Random Early Detection ◽

Modeling And Analysis ◽

Management Scheme

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An Exponential Active Queue Management Method Based on Random Early Detection

Journal of Computer Networks and Communications ◽

10.1155/2020/8090468 ◽

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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