Abstract
The traditional rule-based congestion control algorithms cannot set congestion window size flexibly, resulting in the inadaptation of the dynamic networks. This article presents a method to model end-to-end TCP congestion control problem using classification techniques. The network status parameters as the input and the type of network status as the output are defined through the analysis of some existing congestion control algorithms. NewReno, CUBIC, and Compound are used as feedback to produce the training data of the XGBoost classifier. The experimental results show that the classifier effectively shapes the strategies of three outstanding congestion control algorithms and almost achieves the same throughput, delay, and fairness. The proposed method makes the congestion control algorithm able to learn from data produced by network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
