Learning-based congestion control simulator for mobile internet education

Mobile Internet enables a huge amount of access requests, leading to severe network congestion. To alleviate congestion in the transmission layer, lots of Congestion Control (CC) algorithms have been proposed recently in the research domain, which are specifically designed for various network environments. However, one of the teaching difficulties in mobile Internet education is to allow students to accurately choose the appropriate CC algorithm under the known or measurable network environment.

In this paper, we propose a learning-based CC simulator for mobile Internet education, which provides intuitive suggestions to students on the CC algorithm selections via its learning ability in practical network environments. Our simulator consists of three key modules: the network data module, learning module, and CC module. It has built-in several default CC algorithms and supports students' customized algorithms. The performance of the proposed simulator is evaluated on the implemented simulator prototype with both real and simulated network links. Evaluation results show that the simulator can dynamically select proper CC algorithms in the light of network environments to achieve higher throughput, which benefits students in understanding the working mechanisms of CC algorithms intuitively.