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

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.

I. Introduction

Network softwarisation in the fifth generation of wireless networks (5G) is characterized by significant flexibility and agility as a result of adopting the concepts of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). The former have enabled scalable vertical industry services with strict performance requirements that need to be addressed by MANagement and Orchestration (MANO) systems. Nonetheless, state of the art orchestrators such as ETSI Open Source MANO (OSM MANO) face challenges [1] out of which the NP-Hard [2]–[4] problem of optimal Virtual Network Function (VNF) placement remains essential.