Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach

This work studies a sequential decision making methodology of when to update machine learning models in Edge Computing environments given underlying changes in the contextual data distribution. The proposed model focuses on updates scheduling and takes into consideration the optimal decision time for minimizing the network overhead. At the same time it preserves the prediction accuracy of models based on the principles of the Optimal Stopping Theory (OST). The paper reports on a comparative analysis between the proposed approach and other policies proposed in the respective literature while providing an evaluation of the performances using linear and support vector regression models. Our evaluation process is realized over real contextual data streams to reveal the strengths and weaknesses of the proposed strategy.