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pChanger
In this thesis, we introduced pChanger, a programmable heavy changer prediction method based on time slots. This approach enables data planes on programmable switches to predict heavy changers by balancing performance and resource utilization, employing supervised machine learning techniques.The evaluation results indicate that pChanger achieved an accuracy of 99% and a recall of 75% on the data plane with near-linear runtime for predictions.