scholarly journals QoS Adaptive Flow based Active Queue Management Algorithm and Performance Analysis

2010 ◽  
Vol 10 (3) ◽  
pp. 80-91
Author(s):  
Hyun-Myoung Kang ◽  
Hoan-Suk Choi ◽  
Woo-Seop Rhee
Keyword(s):  
Performance Analysis ◽  
Active Queue Management ◽  
Queue Management ◽  
Management Algorithm ◽  
And Performance

Design and Performance Analysis of a Sliding-mode Prediction Based Active Queue Management

2013 ◽  
Vol 12 (7) ◽  
pp. 1309-1317
Author(s):  
Zhang Xizheng ◽  
Wang Yaonan
Keyword(s):  
Performance Analysis ◽  
Sliding Mode ◽  
Active Queue Management ◽  
Queue Management ◽  
And Performance

scholarly journals Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

2020 ◽  
Vol 53 (5) ◽  
pp. 637-644
Author(s):  
Fuchun Jiang ◽  
Chenwei Feng ◽  
Chen Zhu ◽  
Yu Sun
Keyword(s):  
Reinforcement Learning ◽  
Network Performance ◽  
Task Force ◽  
Active Queue Management ◽  
Queue Management ◽  
Core Network ◽  
Optimal Control Strategy ◽  
Management Algorithm ◽  
Service Load ◽  
And Performance

In the information society, data explosion has led to more congestion in the core network, dampening the network performance. Random early detection (RED) is currently the standard algorithm for active queue management (AQM) recommended by the Internet Engineering Task Force (IETF). However, RED is particularly sensitive to both service load and algorithm parameters. The algorithm cannot fully utilize the bandwidth at a low service load, and might suffer a long delay at a high service load. This paper designs the reinforcement learning AQM (RLAQM), a simple and practical variant of RED, which controls the average queue length to the predictable value under various network loads, such that the queue size is no longer sensitive to the level of congestion. Q-learning was adopted to adjust the maximum discarding probability, and derive the optimal control strategy. Simulation results indicate that RLAQM can effectively overcome the deficiency of RED and achieve better congestion control; RLAQM improves the network stability and performance in complex environment; it is very easy to migrate from RED to RLAQM on Internet routers: the only operation is to adjust the discarding probability.

Stability and Performance Analysis of Compound TCP With REM and Drop-Tail Queue Management

2016 ◽  
Vol 24 (4) ◽  
pp. 1961-1974 ◽  
Author(s):  
Gaurav Raina ◽  
Sreelakshmi Manjunath ◽  
Sai Prasad ◽  
Krishnamurthy Giridhar
Keyword(s):  
Performance Analysis ◽  
Queue Management ◽  
And Performance

Active Queue Management Algorithm for TCP Networks with Integral Backstepping and Minimax

2019 ◽  
Vol 17 (4) ◽  
pp. 1059-1066 ◽  
Author(s):  
Zan-Hua Li ◽  
Yang Liu ◽  
Yuan-Wei Jing
Keyword(s):  
Active Queue Management ◽  
Queue Management ◽  
Management Algorithm

An adaptive active queue management algorithm

2013 ◽  
Author(s):  
Hongcheng Huang ◽  
Gaofei Xue ◽  
Yuanming Wang ◽  
Hongmei Zhang
Keyword(s):  
Active Queue Management ◽  
Queue Management ◽  
Management Algorithm
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