A Choice of Optimal Packet Dropping Function for Active Queue Management

2010 ◽  
pp. 199-206 ◽  
Author(s):  
Dariusz Rafał Augustyn ◽  
Adam Domański ◽  
Joanna Domańska
Keyword(s):  
Active Queue Management ◽  
Queue Management ◽  
Packet Dropping ◽  
Dropping Function

scholarly journals A Simple Mechanism for Throttling High-Bandwidth Flows

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Chia-Wei Chang ◽  
Bill Lin
Keyword(s):  
Active Queue Management ◽  
Low Complexity ◽  
Queue Management ◽  
Packet Dropping ◽  
Rate Estimation ◽  
Simple Mechanism ◽  
High Bandwidth ◽  
Heavy Hitter ◽  
Management Techniques

This letter presents BREATHe, a simple packet dropping scheme for identifying and throttling unresponsive or misbehaving high-bandwidth flows during times of congestion. BREATHe is different from the existing active queue management techniques in that it uses heavy-hitter set analysis to identify highbandwidth flows rather than sampling or rate estimation. Specifically, BREATHe uses heavy-hitter set analysis to detect highbandwidth flows that exceed some target rate and preferentially drop packets from these flows. We show that the proposed mechanism is effective at throttling high-bandwidth flows using a small amount of state and low-complexity operation.

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.

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
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