research-article

A Parametric Study for Congestion Control in Queuing Networks

Authors:

Mazhar Hussain Malik,

Mehmet Emin Aydin,

Mubasher Hussain MalikAuthors Info & Claims

ICFNDS '17: Proceedings of the International Conference on Future Networks and Distributed Systems

Article No.: 47

https://doi.org/10.1145/3102304.3105574

Published: 19 July 2017 Publication History

Abstract

Congestion avoidance mechanisms are used to prevent the saturation of the bottleneck under mean-field domain where large numbers of TCP connections interact with one another. It is clear that selections of parameters play an important role on the performance of networks. In the queuing networks, where numbers of TCP connection are too many, there is very high level of incoming traffic at the bottleneck. In order to avoid the congestion at the bottleneck, Random Early Detection (RED) algorithm is implemented and proven of good performance. To improve the existing performance, reported in previous works, this study demonstrates the impact of constant parameters on the performance of queuing networks and justifies with simulation results.

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Cited By

Durr-e-Nayab Zafar MAltalbe A(2021)Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning TechniquesIEEE Access10.1109/ACCESS.2021.30678169(47033-47047)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3067816

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ICFNDS '17: Proceedings of the International Conference on Future Networks and Distributed Systems

July 2017

325 pages

ISBN:9781450348447

DOI:10.1145/3102304

Copyright © 2017 ACM.

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Published: 19 July 2017

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ICFNDS '17

ICFNDS '17: International Conference on Future Networks and Distributed Systems

July 19 - 20, 2017

Cambridge, United Kingdom

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Cited By

Durr-e-Nayab Zafar MAltalbe A(2021)Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning TechniquesIEEE Access10.1109/ACCESS.2021.30678169(47033-47047)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3067816

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