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Exploring TCP Congestion Control: A Comprehensive Analysis of Cubic, RENO, and VENO Algorithms Using Hybrid Methodology and Machine Learning

  • 2024
  • OriginalPaper
  • Chapter
Published in:
Authors
S. Thangam
Angelina George
Alphonsa Jose
F. Ashik
Sivaneasan Balakrishnan
Published in
Innovative Computing and Communications
Publisher
Springer Nature Singapore

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Abstract

The chapter delves into the critical analysis of TCP congestion control algorithms, focusing on Cubic, RENO, and VENO. It employs a hybrid methodology that combines human analysis with machine learning models, such as CNN and RNN, to evaluate the performance of these algorithms. The study utilizes Wireshark for packet analysis and Lua scripting for customized data extraction. The findings reveal distinct behaviors and patterns in congestion control, highlighting the strengths and weaknesses of each algorithm. The chapter also discusses the limitations of current datasets and models, emphasizing the need for further research to enhance understanding and performance in TCP congestion control.
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previous chapter Smart Greenhouse Management System Using IoT and Multivariate Fuzzy Logic
next chapter A Novel Technique for the Early Diagnosis of Mental Health Using Natural Language Processing
Title
Exploring TCP Congestion Control: A Comprehensive Analysis of Cubic, RENO, and VENO Algorithms Using Hybrid Methodology and Machine Learning
Authors
S. Thangam
Angelina George
Alphonsa Jose
F. Ashik
Sivaneasan Balakrishnan
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Innovative Computing and Communications

Print ISBN: 978-981-9742-27-1

Electronic ISBN: 978-981-9742-28-8

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-4228-8_19
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