Using machine learning techniques to predict device buffer status

Abstract

Devices, systems, methods, and processes are described herein that can reduce network communication overhead by reducing data that is sent. This reduction in data can be achieved by reducing the requests for data related to the buffers of one or more network devices. This buffer status data reduction can be achieved by predicting the current status of the buffers in other network devices. When the buffer predictions are sufficient, the need to request or poll for buffer status reports can be reduced. The buffer predictions can be evaluated through one or more confidence levels. These buffer predictions and confidence levels can be generated by one or more machine learning processes that are configured to operate on one or more network devices. Based on the confidence levels of the predictions, the number of buffer status report polls and of any unsolicited buffer service reports can be reduced.