Adaptive Interference-Aware VNF Placement for Service-Customized 5G Network Slices

Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the diverse performance requirements among different 5G scenarios, an adaptive VNF placement approach is needed to automatically accommodate to service-specific requirements. In this paper, we tackle the VNF placement problem by first proposing a general 5G network slice framework, which jointly contains both edge cloud and core cloud servers. Specially, based on the fact that VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantity the VNF interference. With an aim to maximize the total throughput of accepted requests, we propose an Adaptive Interference-Aware (AIA) heuristic approach to automatically place VNFs in 5G service-customized network slices. Through simulations on two typical 5G scenarios, we demonstrate that AIA can efficiently handle traffic variation especially caused by VNF interference and improve the total throughput by 20.11% and 24.21% in autonomous driving and 4K/8K HD video network slices as compared with the state-of-the-art methods.

I. Introduction

Expected to meet the demands of ultra-high speed, ultra-low latency and high connection density, 5G network is envisioned to be a multi-service network architecture supporting a wide range of vertical use cases, such as massive Internet of things (IoT), remote machinery, autonomous driving and virtual reality (VR) [1]. However, the current one-size-fits-all evolved packet core (EPC) network gradually becomes inefficient to handle diverse service requirements and numerous device types [2]. This impels the progress on updating the network architecture with the capabilities of on-demand networking and flexible service deployment for various scenarios.