MACHINE LEARNING TECHNIQUES FOR IMPLEMENTING TREE-BASED NETWORK CONGESTION CONTROL

    公开(公告)号:US20240007403A1

    公开(公告)日:2024-01-04

    申请号:US18298949

    申请日:2023-04-11

    CPC classification number: H04L47/127 H04L41/16

    Abstract: In various embodiments, a congestion control modelling application automatically controls congestion in data transmission networks. The congestion control modelling application executes a trained neural network in conjunction with a simulated data transmission network to generate a training dataset. The trained neural network has been trained to control congestion in the simulated data transmission network. The congestion control modelling application generates a first trained decision tree model based on an initial loss for an initial model relative to the training dataset. The congestion control modelling application generates a final tree-based model based on the first trained decision tree model and at least a second trained decision tree model. The congestion control modelling application executes the final tree-based model in conjunction with a data transmission network to control congestion within the data transmission network.

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