IVCN: Information-Centric Network Slicing Optimization Based on NFV in Fog-Enabled RAN

Abstract

Content service has become the most popular service that occupies plenty of caching and networking resources. In order to deliver content to end users with high QoE and low infrastructure cost, network function virtualization is deemed as a promising solution for operators to provide content service with geographically dispersed nodes in large scale, which paves the way towards the virtualized content network. In this paper, from the point of view of information-centric networking and service provision, an information-centric virtual content network (IVCN) slicing framework is proposed. A method on the VNF partition from the point of view of information-centric networking and service provision is put forward. The service function chaining and optimization of IVCN are investigated both on the data plane and on the control plane. The IVCN slicing is modeled as a use case in heterogeneous fog-enabled RAN. A method of performance optimization on the IVCN slicing is proposed based on SFC, which aims to optimize the mapping of virtual network functions and virtual content placement. A heuristic approach called IVCN-RANO is proposed to solve the NP-hard problem based on ant colony optimization algorithm. The performance of the IVCN-RANO is evaluated comparing with CEE-LRU, Prob-LRU, and popularity-based schemes. The simulation results reveal that IVCN-RANO outperforms on performances including hit rate, average weighted hops, and average content redundancy.