Self-tuning random early detection algorithm to improve performance of network transmission
type: Item
instance of: scholarly article
title: Self-tuning random early detection algorithm to improve performance of network transmission
zbMATH Open document ID: 1202.94148
DOI: 10.1155/2011/872347
author: Jianyong Chen
author: Cunying Hu
author: Zhen Ji
published in: Mathematical Problems in Engineering
publication date: 2010-12-23T00:00:00Z
full work available at URL: https://eudml.org/doc/231034
review text: Summary: We use a discrete-time dynamical feedback system model of TCP/RED to study the performance of Random Early Detection (RED) for different values of control parameters. Our analysis shows that the queue length is able to keep stable at a given target if the maximum probability \(p_{\max}\) and exponential averaging weight \(w\) satisfy some conditions. From the mathematical analysis, a new self-tuning RED is proposed to improve the performance of TCP-RED network. The appropriate \(p_{\max}\) is dynamically obtained according to history information of both \(p_{\max}\) and the average queue size in a period of time. \(w\) is properly chosen according to a linear stability condition of the average queue length. From simulations with \(ns\)-2, it is found that the self-tuning RED is more robust to stabilize queue length in terms of less deviation from the target and smaller fluctuation amplitude, compared to adaptive RED, Random Early Marking (REM), and Proportional-Integral (PI) controller.
Mathematics Subject Classification ID: 94A13
Mathematics Subject Classification ID: 93E10
zbMATH DE Number: 5829265
Wikidata QID: Q58693609
MaRDI profile type: Publication
OpenAlex ID: W2068153287
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description: scientific article; zbMATH DE number 5829265