Performance Analysis of the Proposed Adaptive Gentle Random Early Detection Method under NonCongestion and Congestion Situations

2011 ◽  
pp. 592-603 ◽  
Author(s):  
Hussein Abdel-jaber ◽  
Jafar Ababneh ◽  
Fadi Thabtah ◽  
Amjad M. Daoud ◽  
Mahmoud Baklizi
Keyword(s):  
Performance Analysis ◽  
Early Detection ◽  
Detection Method ◽  
Random Early Detection

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>

Random Early Detection method for ABR service

2008 ◽  
Vol 34 (4) ◽  
pp. 290-308
Author(s):  
Mohsen Hosamo ◽  
S.P. Singh ◽  
Anand Mohan
Keyword(s):  
Early Detection ◽  
Detection Method ◽  
Random Early Detection

scholarly journals Modeling and Performance Analysis of Dynamic Random Early Detection (DRED) Gateway for Congestion Avoidance

10.28945/2920 ◽  
2005 ◽  
Author(s):  
A. A. Akintola ◽  
G. A. Aderounmu ◽  
L. A. Akanbi ◽  
M. O. Adigun
Keyword(s):  
Performance Analysis ◽  
Early Detection ◽  
Early Response ◽  
Congestion Avoidance ◽  
The Internet ◽  
Random Early Detection ◽  
Queue Size ◽  
Global Synchronization ◽  
Improved Performance ◽  
And Performance

One of the most prominent congestion avoidance schemes in the Internet architecture is the Random Early Detection (RED) algorithm. Several modifications and enhancements have been made to the original RED so as to make it more responsive to congestion avoidance at the gateways. In this paper, we introduced the Dynamic Random Early Detection (DRED) model, which uses a newly introduced parameter i.e. warning line. A robust and efficacious technique to measure the burstiness of incoming traffic has been developed and tested. This involves the estimation of the average queue size, avg, which is dynamically adjusted hence the name of our scheme. The empirical results obtained from the simulations show that our DRED scheme responds early enough to the increased number of packets at the gateway. Also, the maximum drop probability of packets show improved performance over the original RED. It was concluded that our scheme demonstrated superiority by avoiding global synchronization and there is great reduction in the fluctuations of the actual queue size. Also, its early response avoids buffer overflow at the gateways when the queue is near full.

IGRED: An Improved Gentle Random Early Detection Method for Management of Congested Networks

2019 ◽  
Vol 19 (02) ◽  
pp. 1950004
Author(s):  
HUSSEIN ABDEL-JABER ◽  
ABDULAZIZ SHEHAB ◽  
MOHAMED BARAKAT ◽  
MAGDI RASHAD
Keyword(s):  
Early Detection ◽  
Queue Length ◽  
Detection Method ◽  
Random Early Detection ◽  
Queuing Delay ◽  
Packet Dropping ◽  
Dropping Probability ◽  
Maximum Value ◽  
Congested Networks ◽  
Mean Queue Length

Controlling congested router buffers of a network has a crucial role in improving network’s performance. This paper proposes a novel Active Queue Management (AQM) method named Improved Gentle Random Early Detection (IGRED) that based on GRED algorithm, which counted as one of the popular AQM methods. The proposed is mainly developed to overcome the problems faced with classic GRED. The initial packet-dropping probability depends on several parameters such as the average queue length, maximum value of packet dropping probability, minimum and maximum thresholds, etc. IGRED reduces its reliance on the GRED’s parameters through shrinking these parameters. The results shows, when congestion is taken place, the proposed IGRED provides more satisfactory performance with reference to mean queue length, average queuing delay, and overflow packet loss probability.

scholarly journals Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature

2021 ◽  
Vol 18 (15) ◽  
pp. 8123
Author(s):  
Emma K. Austin ◽  
Carole James ◽  
John Tessier
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Detection Method ◽  
Detection Methods ◽  
X Rays ◽  
Review Of The Literature ◽  
Occupational Lung Disease ◽  
Work Related ◽  
Growing Body ◽  
Future Work

Pneumoconiosis, or occupational lung disease, is one of the world’s most prevalent work-related diseases. Silicosis, a type of pneumoconiosis, is caused by inhaling respirable crystalline silica (RCS) dust. Although silicosis can be fatal, it is completely preventable. Hundreds of thousands of workers globally are at risk of being exposed to RCS at the workplace from various activities in many industries. Currently, in Australia and internationally, there are a range of methods used for the respiratory surveillance of workers exposed to RCS. These methods include health and exposure questionnaires, spirometry, chest X-rays, and HRCT. However, these methods predominantly do not detect the disease until it has significantly progressed. For this reason, there is a growing body of research investigating early detection methods for silicosis, particularly biomarkers. This literature review summarises the research to date on early detection methods for silicosis and makes recommendations for future work in this area. Findings from this review conclude that there is a critical need for an early detection method for silicosis, however, further laboratory- and field-based research is required.

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