LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

TL;DR

This paper introduces LACO, a novel latency-driven network slicing orchestration solution for beyond-5G networks that adaptively manages resources to meet diverse SLAs without prior demand knowledge.

Contribution

It presents a multi-armed-bandit-based orchestrator that leverages system structure information for efficient, adaptive radio slicing in heterogeneous, multi-tenant environments.

Findings

LACO achieves near-optimal resource allocation in simulations.

The solution guarantees latency and throughput for diverse slices.

LACO operates efficiently with affordable computational resources.

Abstract

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides-latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based(MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions:near-optimal results within affordable computational time are measured when LACO is in place.