Machine-Learning-Based Prediction and Optimization of Mobile Metro-Core Networks

We propose a methodology to optimize the decisions of mobile metro-core network orchestration systems. We use machine-learning-based traffic prediction to dynamically provision resources in advance. Resource allocation and reconfigurations are calculated through a heuristic that combines reinforcement learning and mixed integer linear programming.