TY - GEN
T1 - Large Scale Network Traffic Optimization Algorithm Based on Differential Equations
AU - Wang, Nan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - With the rapid growth of Internet users and data traffic, frequent congestion and high latency issues in large-scale networks challenge network performance and user experience. This article proposes a network traffic optimization algorithm based on differential equation theory. It first establishes a differential equation model to describe traffic dynamics between network nodes, then derives an optimal control strategy through stability analysis. An iterative algorithm is designed for real-time traffic adjustment and optimization. Experimental results demonstrate that the algorithm improves throughput and stability. Compared to TCP Reno and TCP Vegas, the differential equation-based algorithm achieves an average throughput of 72.3 Mbps and recovers congestion control to 9.5 Mbps within 3 seconds. It also adapts better to delays and maintains stable throughput even with a delay of up to 200ms. The research confirms the effectiveness of differential equation-based algorithms for optimizing network traffic, offering new methods to enhance large-scale network performance.
AB - With the rapid growth of Internet users and data traffic, frequent congestion and high latency issues in large-scale networks challenge network performance and user experience. This article proposes a network traffic optimization algorithm based on differential equation theory. It first establishes a differential equation model to describe traffic dynamics between network nodes, then derives an optimal control strategy through stability analysis. An iterative algorithm is designed for real-time traffic adjustment and optimization. Experimental results demonstrate that the algorithm improves throughput and stability. Compared to TCP Reno and TCP Vegas, the differential equation-based algorithm achieves an average throughput of 72.3 Mbps and recovers congestion control to 9.5 Mbps within 3 seconds. It also adapts better to delays and maintains stable throughput even with a delay of up to 200ms. The research confirms the effectiveness of differential equation-based algorithms for optimizing network traffic, offering new methods to enhance large-scale network performance.
KW - congestion control
KW - differential equation
KW - network traffic optimization
KW - throughput improvement
UR - http://www.scopus.com/inward/record.url?scp=105000108479&partnerID=8YFLogxK
U2 - 10.1109/ICMNWC63764.2024.10871990
DO - 10.1109/ICMNWC63764.2024.10871990
M3 - Conference contribution
AN - SCOPUS:105000108479
T3 - 4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024
BT - 4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024
Y2 - 4 December 2024 through 5 December 2024
ER -