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.
Machine-Learning-Based Prediction and Optimization of Mobile Metro-Core Networks
Alvizu, Rodolfo;Troia, Sebastian;Maier, Guido;Pattavina, Achille
2018-01-01
Abstract
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.File in questo prodotto:
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