CUBIC-LEARN: A REINFORCEMENT LEARNING APPROACH TO CUBIC CONGESTION CONTROL

Ehsan Abedini; ,; Mohsen Nickray; ,

Article Details

CUBIC-LEARN: A REINFORCEMENT LEARNING APPROACH TO CUBIC CONGESTION CONTROL

Authors:

Ehsan Abedini | , | Mohsen Nickray | ,

Publisher:

Princess Sumaya University for Technology

Journal:

Jordanian Journal of Computers and Information Technology Vol.11 Issue 4, pp.0-0

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001937071

pISSN:

2413-9351

Keywords:

Abstract:

 Managing congestion effectively enables reliable and fast data transfer over networks. CUBIC delivers reliable results under normal circumstances, but cannot adapt effectively to changing network scenarios. We introduce CUBIC-Learn, an RL approach for improving congestion control in CUBIC. The central idea is to use a Q- learning algorithm to adjust congestion window thresholds based on current data on packet loss, throughput and latency. Simulations demonstrate more efficient and reliable congestion control when using CUBIC-Learn compared to standard CUBIC. CUBIC-Learn achieves a 47% reduction in packet loss, over a 59% increase in bandwidth utilization, approximately a 28% decrease in retransmissions and 47% lower latency. In addition, CUBIC-Learn shows significant improvements in congestion window (cwnd) growth behavior, fairness among competing flows and stability under heterogeneous traffic and network scenarios, including gigabit-scale bandwidth conditions. Statistical analysis further confirms the robustness of these gains, while the method introduces no additional computational overhead. Overall, CUBIC-Learn performs better than PCC, Reno, Tahoe, NewReno and BBRv3 in most metrics. These findings suggest that RL can markedly improve congestion control in high-speed networks.

Article Details

CUBIC-LEARN: A REINFORCEMENT LEARNING APPROACH TO CUBIC CONGESTION CONTROL

Authors:

Ehsan Abedini | , | Mohsen Nickray | ,

Publisher:

Princess Sumaya University for Technology

Journal:

Jordanian Journal of Computers and Information Technology Vol.11 Issue 4, pp.0-0

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001937071

pISSN:

2413-9351

Q-learning

Reinforcement learning

CUBIC Algorithm

Network congestion

Issues

Article Details

CUBIC-LEARN: A REINFORCEMENT LEARNING APPROACH TO CUBIC CONGESTION CONTROL

Authors: Ehsan Abedini | , | Mohsen Nickray | ,

Publisher: Princess Sumaya University for Technology

Journal: Jordanian Journal of Computers and Information Technology Vol.11 Issue 4, pp.0-0

Document Type: Article

Publication Date: 1/2025

Document Language: English

Document ID (Org No.): 2001937071

pISSN: 2413-9351

Q-learning

Reinforcement learning

CUBIC Algorithm

Network congestion

Issues

Authors:

Ehsan Abedini | , | Mohsen Nickray | ,

Publisher:

Princess Sumaya University for Technology

Journal:

Jordanian Journal of Computers and Information Technology Vol.11 Issue 4, pp.0-0

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001937071

pISSN:

2413-9351