Paper
16 March 2023 Research on TCP congestion window smoothing control algorithm based on traffic awareness
Bing Han, Li-jun Wang, Zhen-liang Li
Author Affiliations +
Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125931B (2023) https://doi.org/10.1117/12.2671663
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China
Abstract
In view of the current TCP congestion control slow-start algorithm and its waste of bandwidth due to short connections, network congestion and packet loss caused by the rapid growth of the congestion window in the later period, this paper studies the slow-start algorithm part of the TCP transmission protocol. Considering the characteristics of the current relatively high-speed network, this paper proposes an improved slow start algorithm with traffic awareness. By statistical analysis of data transmission in the network, the algorithm dynamically determines the initial congestion window size of slow start, and dynamically adjusts the congestion window by tracking the changes of real-time network traffic. In the slow start stage, the smoothness of the congestion window is further analyzed, and the smoothness of the window growth is corrected in real time, so that the congestion window does not increase exponentially, but increases by a more efficient power function. The results of this experiment show that the improved algorithm slows down the growth rate of the congestion window and improves the smoothness of the window growth. It also significantly improved the data transmission rate and throughput.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bing Han, Li-jun Wang, and Zhen-liang Li "Research on TCP congestion window smoothing control algorithm based on traffic awareness", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125931B (16 March 2023); https://doi.org/10.1117/12.2671663
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KEYWORDS
Data transmission

Analytical research

Internet

Network security

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