Optimizing Resource Allocation and VNF Embedding in RAN Slicing

TL;DR

This paper develops and evaluates four algorithms to optimize resource allocation and VNF embedding in 5G RAN slicing, enhancing network efficiency and robustness.

Contribution

It introduces a theoretical foundation and four novel algorithms for efficient VNF mapping and resource allocation in RAN slicing.

Findings

Algorithms improve resource utilization in RAN slices.

Proposed methods demonstrate robustness in extensive experiments.

Enhanced VNF embedding efficiency compared to baseline approaches.

Abstract

5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate's resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efficiently. In particular, we propose four efficient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also conducted to validate the robustness of RAN slicing via the proposed algorithms.