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This work is built on the congestion avoidance model in software-defined networks by employing an efficient algorithm to forecast load and modify methods used in data communication. Because of its versatility, cutting-edge technology like artificial intelligence may be included, improving network performance and offering superior solutions to big issues. In particular, the congestion avoidance problem and the QoS are the advanced approaches that form the basis of the combined algorithm that is proposed for this research in order to achieve more efficient results. The simulations accurately assess the performance of the suggested algorithm, and the results are found in the various evaluation parameters. Because the algorithm is crucial to the network architecture because of the software-defined network's (SDN) adaptability and flexibility, the other outcomes are derived from the simulation's diversity of parameters.

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Leveraging Artificial Neural Networks for Accurate Short-Term Traffic Prediction in Centralized SDN Architecture

  • 会议论文

  • 发表日期：

    2026年

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  • Behdad Bibak,
  • Michel Kadoch,
  • Qia Li,
  • Tao Hong

摘要

This work is built on the congestion avoidance model in software-defined networks by employing an efficient algorithm to forecast load and modify methods used in data communication. Because of its versatility, cutting-edge technology like artificial intelligence may be included, improving network performance and offering superior solutions to big issues. In particular, the congestion avoidance problem and the QoS are the advanced approaches that form the basis of the combined algorithm that is proposed for this research in order to achieve more efficient results. The simulations accurately assess the performance of the suggested algorithm, and the results are found in the various evaluation parameters. Because the algorithm is crucial to the network architecture because of the software-defined network's (SDN) adaptability and flexibility, the other outcomes are derived from the simulation's diversity of parameters.