Based on network function virtualization (NFV) and software defined network (SDN),network slicingis 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 real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

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.

Network function virtualization (NFV) decouples network functions from the dedicated hardware and enables them running on commodity servers, facilitating widespread deployment of virtualized network functions (VNFs). Network operators tend to deploy VNFs in virtual machines (VMs) due to VM's ease of duplication and migration, which enables flexible VNF placement and scheduling. Efforts have been paid to provide efficient VNF placement approaches, aiming at minimizing the resource cost of VNF deployment and reducing the latency of service chain. However, existing placement approaches may result in hardware resource competition of co-located VNFs, leading to performance degradation. In this paper, we present a measurement study on the performance interference among different types of co-located VNFs and analyze how VNFs' competitive hardware resources and the characteristics of packet affect the performance interference. We disclose that the performance interference between co-located VNFs is ubiquitous, which causes the performance degradation, in terms of VNFs' throughput, ranging from 12.36% to 50.3%, and the competition of network I/O bandwidth plays a key role in the performance interference. Based on our measurement results, we give some advices on how to design more efficient VNF placement approaches.