Study of a Smarter AQM Algorithm to Reduce Network Delay
DOI:
https://doi.org/10.5281/zenodo.8071387Keywords:
Neural Network, Testing, Training, DQNAbstract
The focus of our study was to study the behavior of the smart RED algorithm with parameter adaptation based on a neural network structure. Previously Kim et. al. [2] and Basheer et. al. [1], introduced the use of deep reinforcement learning for active queue management (AQM). This work studies the performance deep reinforcement learning, using a simple topology. Transmitters and receivers communicate and all information is routed over a single bottleneck link. This work studies the effect of changing the bottleneck link bandwidth and bottleneck latency on this algorithm. It is observed that increasing training data size will increase the performance of the algorithm. This paper shows a detailed flowchart of the training process and the specific hardware configuration and also demonstrates results.
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