HELICON: Orchestrating low-latent & load-balanced Virtual Network Functions

Abstract

HELICON is a novel hierarchical Reinforcement Learning (RL) approach for orchestrating the dynamic placement of Virtual Network Functions (VNFs) in Cloud and Edge 5G environments. It proves capable of addressing an NP-Hard decision-making problem with adopted RL while augmenting the current state of the art in orchestrators with a previously unexplored lightweight distributed and hierarchical RL approach. HELICON can run as a fully autonomous solution or complement orchestrators, thus bridging a significant gap in existing orchestrators, which generally lack intelligent and dynamic adaptation capabilities. Finally, our performance evaluation results over an actual 5G city testbed and use case validate that HELICON outperforms traditional policy-based Open Source MANO and other heuristic policies concerning single or multi-objective optimisation goals. What is more, HELICON’s performance meets with that of node-specific custom supervised learning models, whereas it clearly outperforms supervised learning under dynamic conditions.