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An Improved TCP Vegas Model for the Space Networks of the Bandwidth Asymmetry

Qixue Guan*, Yueqiu Jiang

College of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
Address: No.6 Nanping Middle Road, Hunnan New District, Shenyang City, Iiaoning Province

* Corresponding Author: Qixue Guan, email

Intelligent Automation & Soft Computing 2020, 26(4), 773-781. https://doi.org/10.32604/iasc.2020.010112

Abstract

It is known that congestion in the reverse direction happens in advance of the congestion in the forward direction due to the significant bandwidth asymmetry in the two directions of the space networks, especially in the satellite networks, which enables the TCP Vegas to enter the phase of the congestion avoidance blindly and reduce the throughput of the forward direction. To solve this problem, a congestion control model, TCP Vegas-DDA, which maintains the frequency of the acknowledgments in the reverse direction is proposed. The model sets the interval time between acknowledgments dynamically based on the variation of the queuing delay in the reverse direction and reduces the impact of the queuing delay on the congestion control algorithm by amending the value of the base round-trip time. Results of the simulation indicate the problem of the low throughput in the forward direction caused by the congestion in the reverse direction can be solved, and the performance of the TCP Vegas in the asymmetric links can be improved.

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APA Style
Guan, Q., Jiang, Y. (2020). An improved TCP vegas model for the space networks of the bandwidth asymmetry. Intelligent Automation & Soft Computing, 26(4), 773-781. https://doi.org/10.32604/iasc.2020.010112
Vancouver Style
Guan Q, Jiang Y. An improved TCP vegas model for the space networks of the bandwidth asymmetry. Intell Automat Soft Comput . 2020;26(4):773-781 https://doi.org/10.32604/iasc.2020.010112
IEEE Style
Q. Guan and Y. Jiang, “An Improved TCP Vegas Model for the Space Networks of the Bandwidth Asymmetry,” Intell. Automat. Soft Comput. , vol. 26, no. 4, pp. 773-781, 2020. https://doi.org/10.32604/iasc.2020.010112

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cc Copyright © 2020 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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