Title: Enhanced computer network performance with decision tree-based congestion control systems

Authors: Hemali Moradiya; Kalpesh Popat

Addresses: Faculty of Computer Applications, Marwadi University, Rajkot, Gujarat, 360003, India ' Faculty of Computer Applications, Marwadi University, Rajkot, Gujarat, 360003, India

Abstract: This study examines internet speed and congestion control to optimise data traffic management. Tahoe, Reno, NewReno, Westwood, Westwood+, and Vegas TCP versions are tested for data transmission issues. Westwood+ substantially increases data transfer. We also examined intelligent congestion control with machine learning. Two decision tree-based supervised learning methods were suggested. The recommended decision tree uses numerical and categorical predictors. Category predictors were less accurate than numerical forecasters in decision tree simulations. NS3 simulations of bus, ring, and dumbbell network topologies' practical effects. Data show Westwood+ works effectively with decision trees. Finally, this research boosts internet speed. It recommends greater research and praises accomplishments. In conclusion, the paper suggests improvements and research. Simulations employed NS3 with Bus, Ring, and Dumbell topologies. This study uses NS3 to emulate Westwood+'s decision tree performance with and without supervised learning. Westwood+ scored higher with decision trees. Our study uses multiple random networks to test its efficacy.

Keywords: Westwood+; computer networks; congestion control systems; network performance; data traffic; bandwidth optimisation; network protocols; traffic management; adaptive systems.

DOI: 10.1504/IJSSE.2026.156330

International Journal of System of Systems Engineering, 2026 Vol.16 No.4, pp.488 - 513

Received: 22 Jan 2024
Accepted: 13 Jun 2024
Published online: 13 Sep 2026 *

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