A Novel Autonomous Profiling Method for the Next-Generation NFV Orchestrators
Shadi Moazzeni, Pratchaya Jaisudthi, Anderson Bravalheri, Navdeep Uniyal, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou
Abstract
Currently, telecommunication research communities are striving towards the adoption of Zero-touch network and Service Management (ZSM) in Network Function Virtualisation (NFV) orchestration. Contemporary efforts on adopting Machine Learning (ML) and Artificial Intelligence (AI) have caused an upsurge of ZSM application in the VNF space. While ML and AI complement the ZSM goals for building the intelligent NFV orchestration, a deep knowledge about the resource consumption by Network Services (NSs) and its constituent Virtual Network Functions (VNFs) is required, which would enable AI and ML models to manage the available resources better and enhance user experience. In this article, we propose a Novel Autonomous Profiling (NAP) method that not only predicts the optimum network load a VNF can support but also estimates the required resources in terms of CPU, Memory, and Network, to meet the performance targets and workload by utilising ML techniques. Our performance evaluation results on real datasets show that the output of NAP can be used in the next generation of NFV orchestration.