An Innovative Active Queue Management Model Through Threshold Adjustment Using Queue Size

2021 ◽  
pp. 257-273
Author(s):  
Soamdeep Singha ◽  
Biswapati Jana ◽  
Sharmistha Jana ◽  
Niranjan Kumar Mandal
Keyword(s):  
Active Queue Management ◽  
Management Model ◽  
Queue Management ◽  
Queue Size ◽  
Threshold Adjustment

HRED, An Active Queue Management Algorithm for TCP Congestion Control

2019 ◽  
Vol 12 (3) ◽  
pp. 212-217
Author(s):  
Nabhan Hamadneh ◽  
Mamoon Obiedat ◽  
Ahmad Qawasmeh ◽  
Mohammad Bsoul
Keyword(s):  
Management Strategies ◽  
Active Queue Management ◽  
Queue Management ◽  
Queue Size ◽  
Management Algorithm ◽  
Link Utilization ◽  
Parameter Configuration ◽  
History Of ◽  
Packet Drop ◽  
Drop Rate

Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.

Deep Reinforcement Learning Based Active Queue Management for IoT Networks

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Minsu Kim ◽  
Muhammad Jaseemuddin ◽  
Alagan Anpalagan
Keyword(s):  
Reinforcement Learning ◽  
Active Queue Management ◽  
Queue Management

A control theoretic approach to active queue management

2001 ◽  
Vol 36 (2-3) ◽  
pp. 203-235 ◽  
Author(s):  
James Aweya ◽  
Michel Ouellette ◽  
Delfin Y Montuno
Keyword(s):  
Active Queue Management ◽  
Queue Management ◽  
Theoretic Approach
Sign in / Sign up

Export Citation Format

Share Document