The Need for Advanced Intelligence in NFV Management and Orchestration

With the constant demand for connectivity at an all-time high, Network Service Providers (NSPs) are required to optimize their networks to cope with rising capital and operational expenditures required to meet the growing connectivity demand. A solution to this challenge was presented through Network Function Virtualization (NFV). As network complexity increases and futuristic networks take shape, NSPs are required to incorporate an increasing amount of operational efficiency into their NFV-enabled networks. One such technique is Machine Learning (ML), which has been applied to various entities in NFV-enabled networks, most notably in the NFV Orchestrator. While traditional ML provides tremendous operational efficiencies, including realtime and high-volume data processing, challenges such as privacy, security, scalability, transferability, and concept drift hinder its widespread implementation. Through the adoption of Advanced Intelligence techniques such as Reinforcement Learning and Federated Learning, NSPs can leverage the benefits of traditional ML while simultaneously addressing the major challenges traditionally associated with it. This work presents the benefits of adopting these advanced techniques, provides a list of potential use cases and research topics, and proposes a bottom-up micro-functionality approach to applying these methods of Advanced Intelligence to NFV Management and Orchestration.

Introduction

In 2012 the European Telecommunications Standards Institute proposed the concept of Network Function Virtualization (NFV) [1]. NFV was initially conceptualized to address challenges faced by Network Service Providers (NSPs) worldwide. With increasing connectivity demands and the increase in network requirements for emerging technologies (i.e., Industrial Internet of Things (IIoT), Intelligent Transportation Systems (ITS), and wearables), NSPs were tasked with expanding their networks to accommodate the growth in demand and improving network performance. This expansion, if conducted, would have accumulated massive amounts of capital and operational expenditures as each network function had its dedicated piece of hardware, and networks were based on ridged physical infrastructure. With the introduction of NFV technology, network functions were abstracted from their dedicated hardware and executed as software-based Virtual Network Functions (VNFs) in servers, data centers, and network clouds.