Optimal Cross Slice Orchestration for 5G Mobile Services

TL;DR

This paper presents an optimal resource allocation framework for 5G network slicing using a Markov decision process, improving service delivery and maximizing provider revenue.

Contribution

It introduces a novel model and MDP-based framework for efficient cross-slice admission control and resource allocation in 5G networks.

Findings

Efficient slice-as-a-service provisioning based on service needs.

Maximized provider revenue through optimal resource management.

Validated framework through simulation results.

Abstract

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed framework and solution are efficient not only in providing slice-as-a-service based on the service requirements, but also in maximizing the provider's revenue.