International Business Machines Corporation
Optimizing machine learning-based, edge computing networks

Abstract:

Optimizing a network comprising a core computing system (CCS) and a set of edge computing devices (ECDs), wherein each of the ECDs locally performs computations based on a trained machine learning (ML) model. A plurality of ML models are continually trained at the CCS, concurrently, based on data collected from the ECDs. One or more states of the network and/or components thereof are monitored. The monitored states are relied upon to decide (when) to change a trained ML model as currently used by any of the ECDs to perform said computations. It may be decided to change the model used by a given one of the ECDs to perform ML-based computations. One of the models as trained at the CCS is selected (based on the monitored states) and corresponding parameters are sent to this ECD. The latter can resume computations according to a trained model.